SEMNASTIK - APTIKOM 2025
type: channel
language: en-US
publication name: SEMNASTIK - APTIKOM 2025
items: Evaluasi MOOC Satisfaction Continuance Model untuk Mengukur Keberlanjutan Penggunaan Platform MOOCs
items: Framework Fenomenografi untuk Requirements Engineering Pendekatan Categories of Requirements Description (CoRD)
items: Optimasi Deep Convolutional Neural Network (DCNN) untuk Klasifikasi Multi-Kelas COVID-19, Pneumonia, dan Paru Normal pada Citra X-ray Toraks
items: Segmentasi Burned Area Menggunakan Model U-Net pada Citra Landsat 9 (Studi Kasus: Sumatera Selatan)
items: Sistem Deteksi PPE (Personal Protective Equipment) Dalam Bidang Konstruksi Berbasis Computer Vision Dengan Metode YOLO
items: Analisis Pola Musiman Saham Bank Mandiri Menggunakan Fuzzy C-Means untuk Optimalisasi Keputusan Investasi Bulanan
items: Aplikasi Panduan Wisata Interaktif Berbasis Android Dengan Penerapan Augmented Reality (Studi Kasus Pada Taman Bukit Raya Desa. Pujo Rahayu Kab. Pesawaran)
items: PERANCANGAN SISTEM INFORMASI TANGGAP BENCANA BERBASIS MASYARAKAT TERPADU (SIGANAS MADU)
items: Studi Perbandingan Analisis: Evaluasi Kinerja Algoritma Klasifikasi pada Dataset Terbatas
items: APLIKASI PENERAPAN METODE PARTITIONING AROUND MEDOIDS (PAM) DALAM SEGMENTASI DESTINASI WISATA UNTUK MENDUKUNG PENGEMBANGAN BERKELANJUTAN
items: Pengembangan Model Deep Learning untuk Prediksi Kesulitan Belajar Matematika Siswa SMA dengan CRISP-DM
items: Implementasi Tong Sampah Otomatis Berbasis Internet of Things (IoT) di Desa Simpang Agung Lampung Tengah
items: Prototype Sistem Informasi Terintegrasi Pelacakan Jejak Dan Peringatan Dini Bahaya Di Kerinci Seblat
items: Perancangan dan Evaluasi User Interface Sistem Informasi Aksara Batak Toba Menggunakan Design Thinking
items: Penerapan Algoritma Genetika sebagai Metode Optimasi Sistem Penjadwalan Perkuliahan
items: Analisis Pemodelan Topik Ulasan Wisatawan Digital: Pendekatan Latent Dirichlet Allocation pada Destinasi Wisata
items: Analisis Sentimen Pengguna Aplikasi Mobile JKN melalui Ulasan Google Play Store Menggunakan Metode SVM
items: Deteksi Anomali pada Lalu Lintas Jaringan Lokal Area Network Menggunakan Metoda Random Forest
items: Sistem Kesulitan Adaptif Berbasis Hibrida ANFIS-Type2 Fuzzy Logic untuk Permainan Memori
items: Analisis Aspek dan Sentimen Terhadap Topik Energi Baru Terbarukan (EBT) di Media Sosial X Menggunakan IndoBERT Multi-Task Learning
items: Sistem Layanan Pemesanan Dan Pencarian Abun Wedding Decoration Menggunakan Algoritma Boyer-Moore
items: Implementasi Algoritma Apriori Untuk Analisis Pola Pembelian Konsumen Pada Umkm Bestea Berbasis Website
items: PERANCANGAN ALAT MONITORING SUHU DAN KELEMBAPAN BERBASIS INTERNET OF THINGS PADA GUDANG BERAS
items: RANCANG BANGUN PEMELIHARAAN TANAMAN HIDROPONIK MELALUI INTERNET OF THINGS (IOT) BERBASIS TENAGA SURYA
items: Desain Framework pengembangan Sistem Informasi Sekolah Tinggi Ilmu Tarbiyah dengan The open group architecture framework (TOGAF)
items: Sistem Pengenalan Motif Songket Melayu Riau Menggunakan Metode Deep Learning
items: Proyeksi Produksi Beras Provinsi Lampung Menggunakan Model Seasonal Autoregressive Integrated Moving Average (SARIMA)

Evaluasi MOOC Satisfaction Continuance Model untuk Mengukur Keberlanjutan Penggunaan Platform MOOCs
type: item
description: Penelitian ini bertujuan untuk mengkaji faktor-faktor yang memengaruhi keberlanjutan penggunaan platform Massive Open Online Courses (MOOCs) dengan mengintegrasikan Information System Success Model (ISSM) dan Expectation-Confirmation Model (ECM) yang dikaji sebagai MOOC Satisfaction Continuance Model. Analisis dilakukan menggunakan metode Partial Least Squares-Structural Equation Modeling (PLS-SEM) yang diimplementasikan dengan Python. Menggunakan 103 data responden aktif pengguna platform MOOCs, hasil penelitian menunjukkan bahwa Perceived Usefulness, Information Quality, dan Hedonic Motivation memberikan pengaruh signifikan terhadap User Satisfaction dan Continued Intention, sedangkan System Quality tidak berpengaruh signifikan terhadap User Satisfaction. Validitas dan relevansi prediktif model yang diterapkan dinyatakan baik melalui pengujian konvergen dan diskriminan. Temuan ini memberikan kontribusi pada pengembangan strategi peningkatan retensi pengguna dan pemahaman faktor-faktor pasca-adopsi dalam sistem pembelajaran dalam jaringan.
creator: Inori Muira Sitanggang
creator: Meida Cahyo Untoro
creator: Mugi Praseptiawan
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 1
ending page: 10

Framework Fenomenografi untuk Requirements Engineering Pendekatan Categories of Requirements Description (CoRD)
type: item
description: Metode persona yang menjadi standar dalam Requirements Engineering memiliki keterbatasan fundamental dalam menangkap keragaman konseptualisasi pengguna. Kajian sistematis menunjukkan 78% implementasi persona mengabaikan diversitas kognitif, mengakibatkan 42% proyek mengalami perubahan requirements signifikan dengan peningkatan biaya hingga 3,5 kali lipat. Penelitian ini mengusulkan fenomenografi sebagai pendekatan alternatif melalui studi empiris platform pembelajaran elektronik dengan 20 partisipan yang menghasilkan 231 unit makna dari 347 halaman transkrip. Framework Categories of Requirements Description (CoRD) yang dikembangkan berhasil mengidentifikasi sepuluh kategori konsepsi berbeda dengan reliabilitas antar-penilai κ = 0.91. Analisis menunjukkan metode persona menghilangkan 67% variasi konseptualisasi kritis. Framework CoRD mentranslasikan variasi menjadi requirements implementable melalui empat komponen terintegrasi dan pendekatan progressive enhancement. Kontribusi utama adalah aplikasi sistematis pertama fenomenografi dalam RE dan framework praktis yang mengatasi limitasi efek rata-rata metode tradisional.
creator: Khidhir
creator: Hedy Pamungkas
creator: Fandy Neta
creator: Ferdilla Ayu Puspita
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 11
ending page: 19

Optimasi Deep Convolutional Neural Network (DCNN) untuk Klasifikasi Multi-Kelas COVID-19, Pneumonia, dan Paru Normal pada Citra X-ray Toraks
type: item
description: Diagnosis COVID-19 yang cepat dan akurat, serta diferensiasinya paru Pneumonia, dan Normal, merupakan tantangan krusial dalam manajemen pandemi dan beban klinis. Interpretasi citra X-ray toraks seringkali menghadapi keterbatasan seperti subjektivitas dan beban kerja yang tinggi. Penelitian ini mengusulkan dan mengoptimalkan Deep Convolutional Neural Network (DCNN) untuk klasifikasi multi-kelas otomatis citra ke dalam tiga kategori yaitu COVID-19, Pneumonia (non-COVID), dan Paru Normal. Kami memanfaatkan tools Teachable Machine menggunakan arsitektur deep learning serta menerapkan strategi optimasi ekstensif pada hyperparameter. Datasset citra dilakukan prapemrosesan secara menyeluruh, termasuk normalisasi dan augmentasi data, untuk memastikan kualitas dan generalisasi model. Dataset dipisah menjadi dua bagian yaitu data latih (training) dan data uji (testing). Persentase data latih 85% dan data uji 15%. Berdasarkan temuan kami, nilai ini memiliki kinerja terbaik. Evaluasi kinerja model dilakukan menggunakan tabel confusion matrix untuk mengevaluasi akurasi pada dataset pengujian. Hasil pengujian menunjukkan bahwa model DCNN mampu memprediksi citra dengan sangat baik. Hasil evaluasi menunjukkan akurasi untuk kategori Covid-19 yaitu 93%, kategori Pneumonia 100%, dan Normal 100%. Hasil ini meningkatkan kepercayaan klinisi dan berkontribusi signifikan terhadap diagnosis medis yang lebih cepat, akurat, dan informatif
creator: Agus Wantoro
creator: Zulkifli Zulkifli
creator: Nur Aminudin
creator: Dwi Feriyanto
creator: Bintang Patra Wijaya
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 20
ending page: 25

Segmentasi Burned Area Menggunakan Model U-Net pada Citra Landsat 9 (Studi Kasus: Sumatera Selatan)
type: item
description: Kebakaran hutan dan lahan (karhutla) merupakan salah satu bencana yang paling sering terjadi di Indonesia, dengan 144 kejadian tercatat pada Agustus 2023. Kondisi ini menegaskan pentingnya penanganan pasca karhutla yang efektif. Penelitian ini bertujuan untuk melakukan segmentasi area terbakar (burned area) menggunakan citra satelit Landsat 9 dengan pendekatan deep learning berbasis model U-Net. Citra diproses dalam bentuk multiband komposit, serta dilengkapi dengan ekstraksi fitur Normalized Difference Vegetation Index (NDVI) dan Normalized Burn Ratio (NBR) sebagai input model. Pelatihan dan evaluasi model dilakukan pada wilayah Sumatera Selatan menggunakan metrik Binary Accuracy, Intersection over Union (IoU), Dice Coefficient, dan Binary Cross Entropy Loss. Hasil evaluasi menunjukkan nilai Binary Accuracy 0.93523, Intersection over Union (IoU) 0.70858, Dice Coefficient 0.82944, dan Binary Cross Entropy 0.18111. Hasil penelitian ini menunjukkan bahwa model U-Net dapat digunakan secara efektif dan efisien dalam mendeteksi area terbakar sebagai bagian dari sistem pemantauan karhutla berbasis citra satelit.
creator: Alber Analafean
creator: Febri Dwi Irawati
creator: Mika Alvionita S
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 26
ending page: 37

Sistem Deteksi PPE (Personal Protective Equipment) Dalam Bidang Konstruksi Berbasis Computer Vision Dengan Metode YOLO
type: item
description: Keselamatan kerja di lingkungan konstruksi merupakan aspek krusial yang harus dijaga melalui kepatuhan terhadap penggunaan Alat Pelindung Diri (APD) atau PPE (Personal Protective Equipment). Penelitian ini bertujuan untuk merancang dan mengembangkan sistem pendeteksi kelengkapan PPE secara otomatis dengan memanfaatkan teknologi Computer Vision berbasis model YOLOv8. Sistem ini diimplementasikan dalam bentuk aplikasi web yang mampu menerima input berupa gambar, video, maupun kamera langsung untuk mendeteksi lima jenis APD utama: helm, rompi, sepatu pelindung, sarung tangan, dan kacamata. Sistem dirancang untuk dapat melakukan deteksi secara real-time dan menyimpan riwayat hasil deteksi secara otomatis ke dalam basis data. Hasil pengujian menunjukkan bahwa model YOLOv8m yang digunakan memberikan performa deteksi tinggi dengan nilai mAP@0.5 sebesar 98.4% dan mAP@0.5:0.95 sebesar 71.7%. Pengujian lapangan juga membuktikan bahwa sistem mampu beroperasi dengan akurasi deteksi rata-rata di atas 90% dalam jarak optimal 1–2 meter, serta kecepatan deteksi di bawah 0.5 detik per frame. Dengan demikian, sistem ini mampu meningkatkan efektivitas pengawasan terhadap standar penggunaan APD secara signifikan di area proyek konstruksi
creator: Ableo Swares
creator: Dian Resha Agustina
creator: Linda Erlinda Sari
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 38
ending page: 51

Analisis Pola Musiman Saham Bank Mandiri Menggunakan Fuzzy C-Means untuk Optimalisasi Keputusan Investasi Bulanan
type: item
description: This study applies the Fuzzy C-Means (FCM) clustering method to identify seasonal patterns in stock price movements with the aim of supporting monthly investment decision-making. The research focuses on Bank Mandiri’s stock (BMRI.JK) over the period 2020–2025. The analysis involves processing historical prices, calculating technical indicators, and implementing fuzzy clustering techniques. Daily price data are transformed into monthly metrics, including average return, win rate, and volatility. The FCM method with three clusters is employed to uncover seasonal tendencies. The results reveal three clearly separated clusters. October, August, and January (Cluster 3) are identified as optimal accumulation periods, with the highest average returns of 0.003148, 0.002889, and 0.002651, respectively, accompanied by superior win rates. In contrast, May, February, December, and September (Clusters 1 and 2) are categorized as profit-taking periods due to their low to negative returns. This study provides a multidimensional perspective on stock performance assessment while offering a methodological contribution through the application of FCM in seasonal pattern recognition. Beyond strengthening the financial literature, the findings also deliver practical value by introducing a data-driven decision support framework for investment strategies, particularly relevant to emerging capital markets such as Indonesia. Keywords : Fuzzy C-Means Clustering; Seasonal Patterns; Investment Decision Support; Stock Market Analysis; Data Mining. Penelitian ini menerapkan metode Fuzzy C-Means (FCM) clustering untuk mengidentifikasi pola musiman pada pergerakan harga saham dengan tujuan mendukung pengambilan keputusan investasi bulanan. Penelitian ini berfokus pada data saham Bank Mandiri (BMRI.JK) dengan rentang waktu 2020–2025. Analisis dilakukan melalui pengolahan harga historis, perhitungan indikator teknis, serta penerapan metode fuzzy clustering. Data harga harian diubah menjadi metrik bul…
creator: Hermawan Manurung
creator: Jaclin Alcavella
creator: Diana Syafithri
creator: M. Syamsuddin Wisnubroto
creator: Fajri Farid
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 52
ending page: 68

Aplikasi Panduan Wisata Interaktif Berbasis Android Dengan Penerapan Augmented Reality (Studi Kasus Pada Taman Bukit Raya Desa. Pujo Rahayu Kab. Pesawaran)
type: item
description: Indonesia boasts vast potential, with significant tourist attractions, natural beauty, diverse cultural heritage, and a diverse community. Bukit Raya Park in Pujo Rahayu Village, Negeri Katon District, Pesawaran Regency (TABURA), is a highly attractive tourist attraction in Lampung Province. However, due to a lack of publicity and ineffective promotional media, it currently offers limited access to road signs, limited social media usage, and word-of-mouth information. This application requires several tools such as Unity, Blender, Vuforia, and Photoshop to develop. The method used is marker-based tracking with a multiple marker technique. The tourist attractions displayed in the application have been tailored to the needs and characteristics of Bukit Raya Park in Pujo Rahayu Village. Testing shows that the application can effectively detect tourist attractions at a maximum distance of 30 cm under certain light intensities. Alpha testing using the black box method also demonstrated the application's functionality, with all 53 buttons functioning as expected.
creator: Eka Ridhawati
creator: Arif Prayoga
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 69
ending page: 77

PERANCANGAN SISTEM INFORMASI TANGGAP BENCANA BERBASIS MASYARAKAT TERPADU (SIGANAS MADU)
type: item
description: Pada penelitian ini dilakukan di Lembaga Badan Pengaduan Bencana Daerah (BPBD) yang bertujuan untuk mengembangkan sistem informasi tanggap bencana berbasis masyarakat terpadu berbasis website dan mobile. Sistem ini dikembangkan untuk memberikan Solusi permasalahan digitalisasi layanan pelaporan bencana, edukasi bencana, dan penyuluhan bencana. Metode yang digunakan yaitu metode agile, dengan pendekatan Scrum, Karena di anggap metode ini lebih fleksibel dalam menghadapi perubahan kebutuhan pengguna secara bertahap. Teknik pengumpulan data di lakukan antara lain seperti wawamcara, identifikasi permasalahan, dan dokumen administrasi lainnya. Dengan adanya sistem ini diharapkan mampu meningkatkan efisiensi laporan bencana, serta memberikan edukasi terkait bencana agar masyarakat lebih waspada dan hati-hati. Hasil akhir berupa platform digital yang digunakan oleh admin melalui website dan masyarakat melalui mobile. Pengujian sistem menunjukan bahwa fitur berjalan dengan baik dan sesuai kebutuhan. Penelitian ini menjadi kontribusi nyata dalam penerapan teknologi informasi untuk Lembaga pengaduan bencana.
creator: Mohammad Iqbal
creator: Rahmat Irsyada
creator: Erick Febriyanto
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 78
ending page: 88

Studi Perbandingan Analisis: Evaluasi Kinerja Algoritma Klasifikasi pada Dataset Terbatas
type: item
description: This research aims to evaluate and compare the performance of various classification algorithms under conditions of limited data quantity. Six algorithms were tested, including Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost, and k-NN against a small-scale public dataset from different domains. Experiments were conducted using a cross-validation technique (k-fold=5), and evaluation was based on confusion matrix to measure performance in terms of accuracy, precision, recall, F1-score, AUC, and model computation time. The results of the comparison of accuracy, precision, and recall performance show that the Naive Bayes algorithm consistently exhibits optimal performance with a value of 0.896. Timing tests show that the Naive Bayes algorithm demonstrates the fastest time in building the model, while the Random Forest algorithm shows the worst time. The AUC test results indicate that the Naive Bayes algorithm excels, followed by k-NN. Meanwhile, SVM shows the worst AUC performance. Based on the f1-score test, the Random Forest and Naive Bayes algorithms demonstrate the best performance, while the Tree algorithm shows the worst performance. This is because the Naive Bayes algorithm has ease of implementation, speed in calculations, and its ability to work well with large, medium, and limited data, as well as with many features. Each user should choose the algorithm tailored to the data used. In addition, the use of Cross-validation has proven to provide a more reliable performance estimate. These findings offer practical recommendations for researchers and practitioners in selecting effective classification algorithms for small-scale datasets, as well as highlighting the importance of validation techniques and data processing in enhancing model generalization under data limitation conditions
creator: Agus Wantoro
creator: Aviv Fitria Yuliana
creator: Dita Septasari
creator: Dwi Yana Ayu Andini
creator: Ikna Awaliyani
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 89
ending page: 94

APLIKASI PENERAPAN METODE PARTITIONING AROUND MEDOIDS (PAM) DALAM SEGMENTASI DESTINASI WISATA UNTUK MENDUKUNG PENGEMBANGAN BERKELANJUTAN
type: item
description: Sustainable tourism is an important aspect of tourism destination development that considers the balance between economic, social, and environmental benefits. This research applies the Partitioning Around Method (PAM) as a clustering technique to group tourists based on their characteristics and preferences. With a case study in Cuku Nyinyi Mangrove forest tourism, this research aims to identify visitor segments, analyze the potential of local resources, and design more effective destination development strategies. The research methodology involved data collection through visitor surveys, interviews with tourism managers, as well as direct observation of local facilities and resources. The data obtained was analyzed using the PAM method to generate different groups of tourists based on the number of visits and activities performed. The results show that the PAM method is able to provide a clear segmentation of tourists, so that managers can optimize services and promotional strategies according to market needs. Keywords : Sustainable Tourism, Partitioning Around Method (PAM), Clustering, Tourism Destinations, Cuku Nyinyi.
creator: Fitria fitria
creator: Tri Melda Yama
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 95
ending page: 105

Pengembangan Model Deep Learning untuk Prediksi Kesulitan Belajar Matematika Siswa SMA dengan CRISP-DM
type: item
description: Rendahnya capaian akademik siswa pada mata pelajaran matematika di tingkat SMA menuntut adanya upaya deteksi dini terhadap kesulitan belajar yang dialami siswa. Penelitian ini mengusulkan pengembangan model prediktif berbasis deep learning neural network untuk mengidentifikasi tingkat kesulitan belajar matematika siswa SMA dengan pendekatan CRISP-DM. Dataset yang digunakan mencakup data akademik (nilai, remedi, aktivitas daring) dan variabel psikologis (motivasi, kecemasan, minat) yang diperoleh dari dua sekolah mitra. Proses pengembangan model meliputi tahap data preprocessing, data augmentation, resampling menggunakan SMOTE, serta pelatihan model dengan fungsi aktivasi ReLU dan Softmax. Untuk mengatasi ketidakseimbangan kelas dan meningkatkan sensitivitas model, digunakan focal loss function dan class weight balancing. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, dan F1-Score. Hasil menunjukkan bahwa model ANN dengan arsitektur empat lapis tersembunyi mampu mencapai akurasi lebih dari 85%, recall ≥ 80%, dan F1-score ≥ 0.80. Penelitian ini menunjukkan bahwa kombinasi data akademik dan psikologis dalam kerangka CRISP-DM dapat menghasilkan model prediksi kesulitan belajar yang andal. Luaran yang dihasilkan meliputi model neural network akurat dan prototipe aplikasi sistem peringatan dini berbasis web, yang dapat digunakan guru untuk melakukan intervensi pembelajaran secara tepat sasaran. Studi ini berkontribusi dalam pemanfaatan kecerdasan buatan untuk meningkatkan kualitas pendidikan melalui sistem pendukung keputusan yang bersifat prediktif dan aplikatif.
creator: Harry Dhika
creator: Surajiyo Surajiyo
creator: Lasia Agustina
creator: Abdul Muchlist
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 106
ending page: 119

Implementasi Tong Sampah Otomatis Berbasis Internet of Things (IoT) di Desa Simpang Agung Lampung Tengah
type: item
description: Household waste is a part of the problems that we often encounter every day in the community environment. One of the causes of the accumulation of waste is the level of public awareness to dispose of waste in its place is still very low, the accumulation of waste that occurs in several places such as on the roadside of the community environment and its surroundings, which can cause environmental problems. Household waste is the remainder of people's daily activities or natural processes in liquid or solid form. Research conducted by Simpang Agung Village, Central Lampung, Waste is unwanted or useless material that is then thrown away by people after completing work or household processes. Due to the lack of awareness of disposing of waste in its place so that the environment is less clean and looks dirty. One of them utilizes the Internet of Things (IoT) which is designed with innovation to create an automatic trash can that will open and close without being touched and the distance of the lid is further from the trash can using sensors and this tool utilizes a distance sensor to measure the distance and volume, then processed using Arduino Uno and will give a command to the servo motor, the movement of the servo motor pulls the lever that has been connected to the rope, so that the trash can open, with the presence of an automatic trash can. Furthermore, this trash can automatically opens when trash is ready to be thrown away and closes when no objects approach. With this automated trash innovation implemented in Simpang Agung Village, Central Lampung, the village environment will be more focused and free of trash.
creator: Sudarmaji
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 120
ending page: 127

Prototype Sistem Informasi Terintegrasi Pelacakan Jejak Dan Peringatan Dini Bahaya Di Kerinci Seblat
type: item
description: This study aims to develop a mobile based Integrated Information System for Hiker Trail Tracking and Early Hazard Warning in Kerinci Seblat National Park (KSNP). The background of this research lies in the high risk of hiking accidents that frequently occur, such as getting lost (67%), severe weather conditions (32%), and the absence of a real-time monitoring system in areas with complex geographical contours and limited communication infrastructure. To overcome these problems, this research produces a prototype of an integrated information system that utilizes mobile device-based GPS technology without requiring additional wearable devices, thereby increasing cost efficiency while still utilizing existing cellular and satellite networks. The system to be developed includes a mobile application with real-time GPS tracking and an emergency panic button, a monitoring dashboard for KSNP officers to manage hiker data and coordinate search and rescue (SAR) operations, as well as historical data analysis to evaluate the carrying capacity of hiking trails and identify hazardous areas. The development method applied is Rapid Application Development (RAD), which enables fast, iterative system construction that remains responsive to field requirements. The outcomes of this study are expected to provide an innovative solution to enhance hiker safety, strengthen risk mitigation systems, and support sustainable mountain tourism management towards the realization of a Smart National Park in Indonesia.
creator: Reni Aryani
creator: Edi Saputra
creator: Mutia Fadhila Putri
creator: Daniel Arsa
creator: Rizqa Raaiqa Bintana
creator: Ulfa Khaira
creator: Mukhtada Billah NST
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 128
ending page: 139

Perancangan dan Evaluasi User Interface Sistem Informasi Aksara Batak Toba Menggunakan Design Thinking
type: item
description: Aksara Batak Toba merupakan salah satu warisan budaya Nusantara yang memiliki nilai historis dan identitas kultural bagi masyarakat Batak. Namun, keberadaannya semakin terancam karena minimnya minat generasi muda dalam mempelajarinya. Seiring perkembangan teknologi digital, diperlukan media pembelajaran yang inovatif, interaktif, dan mudah diakses untuk mendukung upaya pelestarian. Penelitian ini bertujuan merancang dan mengevaluasi sistem informasi Aksara Batak Toba dengan memanfaatkan pendekatan Design Thinking. Proses penelitian dilakukan melalui lima tahapan, yaitu empathize untuk memahami kebutuhan pengguna, define untuk merumuskan masalah, ideate untuk menghasilkan ide solusi, prototype untuk membuat rancangan antarmuka, serta test untuk melakukan uji coba bersama pengguna. Sistem dikembangkan berbasis web dengan fitur utama pengenalan huruf, cara penulisan, serta latihan interaktif. Evaluasi usability dilakukan menggunakan metode System Usability Scale (SUS). Hasil penelitian menunjukkan sistem memperoleh nilai SUS sebesar 70,1 yang termasuk kategori baik. Hal ini membuktikan bahwa pendekatan Design Thinking efektif menghasilkan antarmuka yang intuitif dan sesuai kebutuhan. Kontribusi penelitian ini meliputi: (1) aspek ilmiah, yaitu menambah literatur tentang penerapan Design Thinking dan SUS dalam sistem pembelajaran budaya lokal; (2) aspek praktis, yaitu menghadirkan media pembelajaran interaktif yang mendukung generasi muda mempelajari aksara Batak; (3) aspek sosial-budaya, yaitu memperkuat upaya pelestarian aksara tradisional di era digital; serta (4) aspek inovasi, yakni mengintegrasikan desain berpusat pada pengguna dengan evaluasi usability dalam konteks aksara tradisional. Dengan demikian, sistem informasi berbasis web ini tidak hanya berfungsi sebagai sarana edukasi, tetapi juga sebagai strategi konkret dalam menjaga keberlanjutan warisan budaya Nusantara.
creator: Ranty Deviana Siahaan
creator: Kartika Novia Hutauruk
creator: Iustisia Natalia Simbolon
creator: Herimanto
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 140
ending page: 148

Penerapan Algoritma Genetika sebagai Metode Optimasi Sistem Penjadwalan Perkuliahan
type: item
description: Manual scheduling of lectures in the Computer Science Program at Universitas Muhammadiyah Metro often faces issues such as schedule conflicts, limited room availability, and error-prone, time-consuming processes. This study aims to develop an automated web-based scheduling system using a Genetic Algorithm to optimize time, course, lecturer, and room distribution. The system was developed using the Rapid Application Development (RAD) method and an Object-Oriented Programming (OOP) approach. Python and the Django framework were utilized in the development, with MySQL via XAMPP as the database. The Genetic Algorithm was implemented to find optimal scheduling solutions by evaluating the fitness value of each individual. The system was tested using blackbox and beta testing methods. The results show that the developed system produces efficient and low-conflict schedules, while significantly accelerating the scheduling process. It also supports automatic validation by the 1st Vice Dean and digital distribution of the schedule to lecturers and students. This implementation has made the scheduling process faster, more accurate, and well-documented.
creator: mustika
creator: Amri Reza Wahyudin
creator: Pujianto Pujianto
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 149
ending page: 157

Analisis Pemodelan Topik Ulasan Wisatawan Digital: Pendekatan Latent Dirichlet Allocation pada Destinasi Wisata
type: item
description: The exponential growth of user-generated tourist reviews on digital platforms holds significant potential for understanding traveler experiences and perceptions of destinations. However, the vast and unstructured nature of this textual data requires reliable methods for systematic theme extraction. This study aims to map the dominant topics embedded in digital tourist reviews using the Latent Dirichlet Allocation (LDA) approach. Employing an exploratory descriptive research design, a total of 2,000 reviews from Indonesian tourism destinations were collected and processed through tokenization, normalization, and stemming, before being modeled using Gensim’s LDA. The model was evaluated using the coherence score (c_v), resulting in an optimal model with ten topics (K=10). The identified topics reflect various tourism aspects, including natural beauty, historical heritage, family activities, culinary experiences, and popular landmarks. Topic distribution analysis revealed the spread and proportion of themes across the entire corpus, offering rich contextual insights into tourist narratives. These findings contribute to data-driven destination management and form a foundation for the development of smart tourism applications, while also providing new insights into tourist preferences in Indonesia. The LDA-based thematic mapping demonstrates strong potential in supporting intelligent destination management and enhancing personalized tourism recommendation systems.
creator: Hartatik Hartatik
creator: R Rizal Isnanto
creator: Budi Warsito
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 158
ending page: 163

Analisis Sentimen Pengguna Aplikasi Mobile JKN melalui Ulasan Google Play Store Menggunakan Metode SVM
type: item
description: The JKN Mobile application, launched by BPJS Kesehatan in 2017, aims to facilitate participants' access to healthcare services. However, its implementation presents various challenges for users, and understanding the large number of reviews is challenging due to the numerous steps involved. This study aims to analyze the sentiment of JKN Mobile application users' reviews on the Google Play Store. Review data was collected from November 20, 2024, to May 20, 2025, totaling 9,878 reviews. The research steps included data collection, preprocessing, sentiment labeling using TextBlob, classification using SVM, and evaluation using a confusion matrix. The evaluation results showed the model performed well, with an accuracy of 94% (for the newest and most relevant sort data), and precision, recall, and F1-score values above 0.90 for negative, neutral, and positive sentiment. And the main issues that are most frequently reviewed are obtained from the word cloud visualization of the words that most frequently appear in reviews, such as “Application”, “Verification”, “Register”, “Difficult”, and “Easy”.
creator: Jum Dapi okta
creator: Siti Ulfa Dillah
creator: M.Nang Al Kodri
creator: Anggraeni Agustin Muris
creator: Pujianto
creator: Joko Kuswanto
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 164
ending page: 174

Deteksi Anomali pada Lalu Lintas Jaringan Lokal Area Network Menggunakan Metoda Random Forest
type: item
description: Deteksi anomali pada lalu lintas jaringan menjadi hal yang krusial dalam menjaga keamanan dan kestabilan sistem informasi. Lalu lintas jaringan yang semakin kompleks menuntut adanya metode deteksi yang efektif dan akurat. Penelitian ini bertujuan untuk mendeteksi anomali pada lalu lintas jaringan menggunakan metode Random Forest, yang dikenal memiliki performa tinggi dalam klasifikasi data. Dalam penelitian ini, data lalu lintas jaringan dikumpulkan dan dianalisis berdasarkan fitur IP (Internet Protokol) Source, IP Destination, Protocol, dan Length. Dengan menggunakan metode Random Forest model default didapatkan nilai evaluasi untuk kelas anomali dengan nilai presisi 77%, nilai recall 87% dan f1-score 82%, sedangkan model hyperparameter tuning berhasil mendapatkan nilai presisi 59%, nilai recall 99% dan f1-score 74%. Hasil penelitian ini menunjukkan bahwa metode Random Forest dengan hyperparameter tuning sangat sensitif dalam mendeteksi anomali dengan nilai recall 99%. Temuan ini diharapkan dapat membantu meningkatkan analisis jaringan dengan memberikan peringatan dini terhadap aktivitas mencurigakan atau perubahan pola jaringan
creator: Premitasari Marisa
creator: Rizka Milandga Milenio
creator: Mohamad Yusup Suryaman
creator: Doddy Setiyadi
creator: Choerunnisa Septiani Tri Noerdin
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 175
ending page: 183

Sistem Kesulitan Adaptif Berbasis Hibrida ANFIS-Type2 Fuzzy Logic untuk Permainan Memori
type: item
description: Merancang tingkat yang tepat untuk pemain dengan berbagai tingkat kemampuan adalahtantangan bagi permainan memori. Ketika tingkat kesulitan tetap sama, pemain yang sudah terbiasa dengan permainan menjadi marah, dan pemain yang lebih berpengalaman menjadibosan. Akibatnya, minat pemain berkurang. Mengintegrasikan Sistem Inferensi Neuro-Fuzzyyang dapat disesuaikan (ANFIS) dengan logika fuzzy tipe-2 dalam kerangka hibridamenghasilkan sistem kesulitan yang dapat disesuaikan. Lima level pertama menggunakanANFIS untuk mempelajari cara pemain bermain, dan lima level berikutnya menggunakan logikafuzzy tipe-2 untuk beradaptasi dan mengatasi ketidakpastian. Metode penelitian ini menggunakansimulasi Monte Carlo yang menggabungkan 120 pemain simulasi, menghasilkan 1.200 titik data untuk validasi. Dengan menggunakan TypeScript dan arsitektur modular, setiap pesananmembutuhkan waktu pemrosesan dua hingga tujuh milidetik. Studi tersebut menemukan bahwadiferensiasi sistem adalah 0,681, lebih besar dari ambang batas 0,2 untuk perubahan pemain. Teknologi ini dengan akurasi 94% memindahkan pengetahuan dari fase pembelajaran ke faseadaptasi. Nilai Cohen's d sebesar 1,24 menunjukkan bahwa efeknya signifikan dalam praktiknya. Sebaliknya, sistem ini menunjukkan hasil yang beragam: fase pembelajaran menunjukkanpeningkatan 73,9%, tetapi fase adaptif murni menunjukkan penurunan 42,1% dibandingkandengan garis dasar ANFIS. Studi ini memperkenalkan arsitektur hibrida pertama yang mengintegrasikan ANFIS dengan logika fuzzy tipe-2 untuk industri game; namun, efektivitasnyabergantung pada kondisi aplikasi tertentu.
creator: andi fauzan hediantoro andi
creator: Hedy Pamungkas
creator: Ferdilla Ayu Puspita
creator: Khidhir
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 184
ending page: 194

Analisis Aspek dan Sentimen Terhadap Topik Energi Baru Terbarukan (EBT) di Media Sosial X Menggunakan IndoBERT Multi-Task Learning
type: item
description: Transisi energi terbarukan di Indonesia membutuhkan dukungan publik yang kuat, tetapi memahami persepsi publik melalui media sosial masih menjadi tantangan karena kompleksitas bahasa Indonesia dan keterbatasan model analisis sentimen tugas tunggal yang ada dalam konteks energi terbarukan. Penelitian ini bertujuan untuk menganalisis aspek dan sentimen publik terhadap isu EBT melalui data media sosial X (Twitter) menggunakan pendekatan multi-task learning berbasis IndoBERT. Dataset terdiri dari 1.095 tweet yang dikumpulkan selama akhir tahun 2024, yang diproses melalui text prepocessing, topic modeling dengan Latent Dirichlet Allocation (LDA), pelabelan data dengan bantuan Large Language Model dan validasi manusia, serta klasifikasi aspek dan sentimen menggunakan IndoBERT multi-task learning. LDA mengidentifikasi 11 topik optimal yang dikategorikan menjadi lima aspek yaitu ENVIRONMENTAL_IMPACT, ENERGY_TECHNOLOGY, GOVERNMENT_POLICY, ENERGY_ACCESS, dan OTHER. Analisis sentimen menunjukkan dominasi sentimen negatif, diikuti positif dan netral. Hal ini mengindikasikan kritik dan kekhawatiran publik terhadap implementasi energi terbarukan. Model multi-task IndoBERT mencapai performa unggul dengan akurasi klasifikasi sentimen 89% dan kesepakatan keseluruhan 92.6% untuk klasifikasi aspek. Insights berbasis data ini menyoroti perlunya pergeseran dari pesan lingkungan yang defensif menuju komunikasi proaktif tentang manfaat praktis dan akses energi yang berkeadilan untuk membangun dukungan publik yang lebih kuat bagi transisi energi terbarukan Indonesia.
creator: Amanda Putri Apriliani
creator: Raihan Akira Rahmaputra
creator: Anom Wajawening
creator: Gema Fitri Ramadani
creator: Ariana Yunita
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 195
ending page: 214

Sistem Layanan Pemesanan Dan Pencarian Abun Wedding Decoration Menggunakan Algoritma Boyer-Moore
type: item
description: Perkembangan teknologi informasi mendorong digitalisasi pada berbagai sektor layanan, termasuk industri dekorasi pernikahan. Abun Wedding Decoration masih menghadapi kendala dalam pengelolaan pemesanan dan pencarian layanan yang dilakukan secara manual, sehingga berpotensi menimbulkan kesalahan dan keterlambatan. Penelitian ini bertujuan untuk merancang dan membangun sistem layanan pemesanan dan pencarian dekorasi berbasis web, dengan penerapan algoritma Boyer-Moore sebagai metode pencocokan string untuk mempercepat proses pencarian data. Metode penelitian menggunakan model pengembangan perangkat lunak Waterfall yang meliputi analisis kebutuhan, desain sistem, implementasi, pengujian, serta pemeliharaan. Sistem yang dikembangkan menyediakan fitur utama berupa registrasi, login, pencarian produk, pemesanan, checkout, konfirmasi pembayaran, serta pengelolaan data pelanggan, produk, vendor, dan transaksi. Proses pengujian dilakukan dengan metode Black Box Testing untuk memastikan fungsionalitas sistem, serta pengujian algoritma Boyer-Moore dalam pencarian produk berdasarkan kata kunci. Hasil penelitian menunjukkan bahwa seluruh fitur sistem berjalan sesuai kebutuhan, termasuk kemampuan algoritma Boyer-Moore dalam menemukan produk yang relevan dengan kata kunci pencarian.
creator: M. Arif Prayoga
creator: Septilia Arfida
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 215
ending page: 226

Implementasi Algoritma Apriori Untuk Analisis Pola Pembelian Konsumen Pada Umkm Bestea Berbasis Website
type: item
description: Digital transformation has encouraged micro, small, and medium enterprises (MSMEs) to adopt information technology in managing business activities, particularly in transactions and data analysis. umkm Bestea faces challenges in understanding consumer purchasing patterns, which are essential for marketing strategies and inventory management. Manual analysis is often ineffective due to limited resources and data complexity. Therefore, data mining, especially the Apriori algorithm, is applied as a solution to discover product associations frequently purchased together, thereby improving operational efficiency and supporting data-driven decision-making. This research employed the Extreme Programming (XP) software development method, which consists of planning, design, coding, and testing phases. The dataset comprises 2,129 sales transactions recorded by umkm Bestea between September and December 2024. The Apriori algorithm was used to calculate support and confidence for each itemset to identify frequent itemsets that meet minimum thresholds. A web-based system was developed using the Laravel framework, featuring purchase pattern analysis, product management, stock, composition, and reporting modules. The system was evaluated using black-box testing to ensure that all functionalities worked properly. The results show that the Apriori algorithm successfully identified consumer purchasing patterns, such as the tendency of Iced Green Tea being purchased together with Iced Tea Original or Iced Lemon Tea. The developed system not only supports purchase pattern analysis but also functions as an integrated Point of Sale (POS) application. This research contributes to assisting MSMEs in formulating promotional strategies, optimizing inventory, and enhancing the effectiveness of data-driven decision-making.
creator: Achmad Aldi Sakoni
creator: Septilia Arfida
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 227
ending page: 239

PERANCANGAN ALAT MONITORING SUHU DAN KELEMBAPAN BERBASIS INTERNET OF THINGS PADA GUDANG BERAS
type: item
description: The quality of rice, a strategic commodity in Indonesia, is highly influenced by the temperature and humidity of its storage warehouse. Inefficient manual monitoring in production centers like Pringsewu, Lampung, can lead to quality degradation and threaten food security. This research aims to design an Internet of Things (IoT)-based monitoring system to maintain rice warehouse conditions automatically, accurately, and remotely. Using the prototyping method, the system was built with a NodeMCU ESP8266 microcontroller, a DHT22 sensor for temperature and humidity, and a DS18B20 for high-precision temperature validation. Data is displayed locally on an I2C LCD, automatically logged to Google Sheets, and visualized in real-time via the Blynk app. The system also features an automatic response, using a relay module to activate a cooling fan when the temperature reaches ≥30°C or humidity ≥80% RH. The result is a reliable functional prototype capable of providing accurate data, sending notifications, and performing corrective actions automatically. This system is expected to be a cost-effective and appropriate technological solution for small-scale warehouse managers to minimize post-harvest losses and maintain rice quality.
creator: Satria Nur Ramadan
creator: Nur Aminudin
creator: Tahta Herdian Andika
creator: Ratnasari
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 240
ending page: 244

RANCANG BANGUN PEMELIHARAAN TANAMAN HIDROPONIK MELALUI INTERNET OF THINGS (IOT) BERBASIS TENAGA SURYA
type: item
description: Hydroponics is a method of cultivating plants that does not use soil, relying heavily on water quality and the availability of appropriate nutrients. Manual monitoring of hydroponic system maintenance is not yet efficient because it requires a person's presence periodically in the hydroponic garden to monitor water quality and the availability of nutrient content. This paper proposes a form of online monitoring system design for hydroponic garden maintenance based on the Internet of Things (IoT) using a solar-powered ESP32 microcontroller. This system uses a Total Dissolved Solids (TDS) sensor to monitor water quality, a DS18B20 temperature sensor to monitor water temperature, a DHT11 sensor to monitor environmental humidity, and an ultrasonic sensor to monitor water level. Data from the sensors are transmitted to the Blynk application online, allowing users to monitor via smartphone remotely. This system automatically turns on the nutrient and water pumps based on pre-set parameter values. Test results show that the hydroponic plant maintenance monitoring system via the Internet of Things connected to a smartphone successfully operates according to the pre-set design scenario.
creator: Mohamad Amin
creator: Rizki Galih Saputro
creator: Muhamad Arif Yuana
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 245
ending page: 249

Desain Framework pengembangan Sistem Informasi Sekolah Tinggi Ilmu Tarbiyah dengan The open group architecture framework (TOGAF)
type: item
description: Digital transformation in the higher education sector, particularly at private religious institutions such as the Tarbiyah College of Science (STIT), faces the challenges of information system fragmentation and infrastructure gaps that hamper operational efficiency and competitiveness. This research aims to design a systematic enterprise architecture framework for information system development at STIT by adapting the Open Group Architecture Framework (TOGAF) methodology. A qualitative approach was used, adapting the Architecture Development Method (ADM) phases of TOGAF: Preliminary Phase, Architectural Vision, Business Architecture, Information Systems Architecture (Data & Application), and Technology Architecture. The result is a comprehensive blueprint for a target architecture aligned with STIT's vision, mission, and strategy. The proposed business architecture models integrated operational processes, supported by a centralized data architecture that prevents duplication. At the application layer, an integrated system portfolio—including an integrated SIAKAD (Admissions Information System), a digital PMB portal, and student self-service—is designed to replace fragmented systems. Finally, the technology architecture defines a scalable and secure infrastructure to support the entire system. This framework provides a phased implementation roadmap and recommends ongoing governance to ensure the sustainability of the architecture. It concludes that adopting a customized TOGAF framework provides structured guidance for addressing complex IT challenges at STIT, ensuring technology investments align with the institution's strategic objectives.
creator: Euis mustika Priyanganti
creator: Sukatmi Sukatmi
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 250
ending page: 256

Sistem Pengenalan Motif Songket Melayu Riau Menggunakan Metode Deep Learning
type: item
description: Songket Melayu Riau merupakan salah satu warisan budaya Indonesia yang memiliki beragam motif dengan makna filosofis yang mendalam. Namun, keterbatasan informasi dan sulitnya membedakan motif menyebabkan rendahnya minat generasi muda terhadap kain tradisional ini. Penelitian ini bertujuan mengembangkan sistem pengenalan motif songket Melayu Riau menggunakan metode deep learning berbasis Convolutional Neural Network (CNN). Dataset terdiri atas empat motif utama yaitu pucuk rebung, siku awan, siku keluang, dan tampuk manggis dengan total 420 citra hasil augmentasi. Model CNN dirancang melalui tahapan pre-processing, feature extraction, dan classification, kemudian diimplementasikan pada aplikasi berbasis web. Hasil pengujian menunjukkan akurasi pelatihan sebesar 96,31%, akurasi validasi 94,04%, dan akurasi pada citra baru 93,75%. Temuan ini membuktikan bahwa CNN efektif dalam klasifikasi motif songket, sekaligus mendukung pelestarian budaya Melayu Riau melalui inovasi teknologi
creator: Feri Candra
creator: Annisa Nurul Fajri
creator: Khairul Umam Syaliman
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 257
ending page: 266

Proyeksi Produksi Beras Provinsi Lampung Menggunakan Model Seasonal Autoregressive Integrated Moving Average (SARIMA)
type: item
description: Food security is a strategic issue in Indonesia, including in Lampung Province, which is one of the country’s main rice production centers. Rice self-sufficiency is stated in the 2025–2029 Rencana Pembangunan Jangka Panjang (RPJM). To achieve food sovereignty, the Lampung Provincial Government faces several challenges, including population growth, climate change, land-use conversion, and fluctuations in rice prices. Data from the Badan Pusat Statistik (BPS) show that rice consumption has been increasing annually, yet the existing data have not been fully utilized for food policy formulation. Therefore, an accurate forecasting system is needed to support policy-making. This study employs the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to forecast rice production in Lampung Province, utilizing seasonal sub-round (four-month) data from BPS Lampung. The analysis results indicate that the best model is SARIMA (2,1,0) (0,0,0) [3], with the lowest Akaike Information Criterion (AIC) value. The forecast shows a relatively stable trend compared to previous years, with declines in the early subrounds of 2025 and 2026, followed by increases in the subsequent subrounds. These results can serve as recommendations for local governments in food security planning. However, the SARIMA model has limitations in capturing patterns influenced by external factors such as weather conditions during planting seasons, shifts in planting and harvesting periods, fertilizer shortages, pest and disease outbreaks, and government policies that may affect productivity.
creator: Rizki Abdi Utama
creator: Joko Triloka
rights: Copyright (c) 2025 SEMNASTIK - APTIKOM 2025
date: 2025-12-29
publication date: 2025-12-29
volume: 1
number: 1
starting page: 267
ending page: 277