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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ilmiah KOMPUTASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) Jurnal Riset Informatika Prosiding SNITP (Seminar Nasional Inovasi Teknologi Penerbangan) JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Teknik Informatika (JUTIF) Djtechno: Jurnal Teknologi Informasi International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Sosial dan Abdimas Journal of Information Systems and Technology Research Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Terapan (J-TETA) Journal of Community Service (JCOS) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
idhom, Mohammad
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Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Transportation

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Comparative Analysis of Deep Learning Models for Wind Speed Prediction Using LSTM, TCN and RBFNN Wardani, Firly Setya; Idhom, Mohammad; Aviolla Terza Damaliana
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1298

Abstract

Wind speed forecasting plays a vital role in various sectors, including renewable energy management and disaster preparedness for extreme weather events. Accurate prediction models are essential to support decision-making processes, especially in regions with dynamic seasonal patterns. This study compares the performance of three time series prediction models Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Radial Basis Function Neural Network (RBFNN) for forecasting daily wind speed. The dataset consists of historical wind speed data that underwent multiple preprocessing steps, including seasonal-based missing value imputation, stationarity testing, supervised transformation, normalization, and hyperparameter tuning to optimize model performance. The models were evaluated using four standard regression metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-Squared (R²), and Mean Absolute Percentage Error (MAPE). The results show that the TCN model outperformed the others, achieving an MAE of 1.117, RMSE of 1.524, R² of 0.120, and MAPE of 20.95%. The LSTM model ranked second with competitive performance, while the RBFNN model produced consistent but slightly lower accuracy. The findings highlight the superiority of TCN in capturing complex sequential and seasonal patterns in wind speed data. The unique contribution of this research lies in integrating seasonal-based preprocessing with a comparative evaluation of three advanced models under varying conditions, including extreme weather scenarios. This study serves as a foundation for developing more accurate and reliable wind speed forecasting systems to support renewable energy planning and enhance disaster risk mitigation strategies.
Perbandingan Kinerja LSTM dan GA-LSTM dalam Prediksi Curah Hujan Harian sebagai Strategi Mitigasi Bencana Banjir di Jawa Timur Linggasari, Dienna Eries; Idhom, Mohammad; Trimono, Trimono
Jurnal Ilmiah Komputasi Vol. 24 No. 3 (2025): Jurnal Ilmiah Komputasi : Vol. 24 No 3, September 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.24.3.3833

Abstract

Curah hujan merupakan salah satu parameter iklim penting yang sangat memengaruhi keseimbangan lingkungan dan kehidupan manusia, khususnya di daerah tropis seperti Surabaya. Variabilitas curah hujan yang tinggi dapat memicu bencana banjir, sehingga prediksi curah hujan yang akurat menjadi langkah penting dalam upaya mitigasi. Namun, karakteristik curah hujan yang bersifat non-linear, musiman, dan mengandung banyak fluktuasi acak menjadikan prediksi ini sebagai tantangan tersendiri. Penelitian ini bertujuan untuk membandingkan performa model Long Short-Term Memory (LSTM) dan LSTM yang dioptimasi dengan algoritma genetika (GA-LSTM) dalam memprediksi curah hujan harian di Surabaya. Data yang digunakan merupakan data curah hujan harian dari BMKG Surabaya selama periode 2020–2024. Metode penelitian mencakup preprocessing data, pembentukan sekuens, pelatihan model LSTM, optimasi hyperparameter menggunakan GA, serta evaluasi model dengan metrik MSE, RMSE, dan MAE. Hasil penelitian menunjukkan bahwa model GA-LSTM memberikan hasil prediksi yang lebih akurat dengan nilai MSE sebesar 0.0060, dibandingkan dengan LSTM standar sebesar 133.33. Performa GA-LSTM yang lebih stabil dalam menangani fluktuasi ekstrem menunjukkan bahwa pendekatan optimasi berbasis evolusi efektif dalam meningkatkan akurasi prediksi deret waktu curah hujan. Hasil ini diharapkan dapat menjadi referensi ilmiah bagi perumusan kebijakan mitigasi banjir berbasis data.
Integrating IndoBERTweet and GRU for Opinion Classification on X Towards Public Transportation in Jakarta Nafiah, Fajria Ulumin; Panglima, Talitha Fujisai; Idhom, Mohammad; Trimono, Trimono
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10723

Abstract

Jakarta, the capital of Indonesia, faces persistent challenges with its public transportation system due to rapid urbanization, increased use of private vehicles, and poor service quality. While social media platforms such as X (formerly Twitter) offer valuable insights into public opinion, their unstructured nature complicates analysis. This study uses deep learning models to categorize user sentiments into six labels that cover positive and negative aspects of comfort, safety, and punctuality. The results show that IndoBERTweet achieved the highest performance, with 95.43% accuracy and a macro F1-score of 0.9545. It also required the shortest training time, at six minutes and 30 seconds. IndoBERTweet+GRU followed closely behind with an accuracy of 94.62% and a macro F1-score of 0.9460 in six minutes and 50 seconds. This shows that adding a GRU layer provides competitive results, but does not surpass the baseline model. Error analysis revealed that, while the models performed well with explicit sentiments, the models struggled with implicit expressions, such as sarcasm and mixed opinions. These results demonstrate the potential of sentiment analysis in real-time monitoring systems, which could help policymakers identify urgent issues and support data-driven improvements in Jakarta’s urban transportation services.
Optimization of Palm Fruit Ripeness Detection With Yolov11 on CPU Anniswa, Iqbal Ramadhan; JAUHARIS SAPUTRA, Wahyu Syaifullah; Idhom, Mohammad; Rizaldy Pratama, Alfan; Susrama Mas Diyasa, I Gede
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111253

Abstract

The palm oil industry is one of the strategic sectors that contributes significantly to the Indonesian economy. However, this industry still faces various challenges, particularly in terms of operational efficiency and the implementation of digitalization, especially at the level of independent farmers who often still use manual methods to determine the ripeness of the fruit. This manual process is prone to subjectivity, which can impact harvest quality and supply chain efficiency. To address this issue, this study proposes a palm oil fruit ripeness detection system based on the YOLOv11 algorithm, chosen for its advantages in inference speed and detection accuracy, especially when run on devices with limited resources. The developed model was then implemented using the ONNX Runtime Framework. This enables accelerated inference processes and supports portability on hardware with limited resources. Test results show that the model achieves an mAP@50 accuracy of 90.2% with an average latency of around 255 ms to 300 ms. With these achievements, this system is not only reliable in detecting fruit ripeness, but also efficient in processing time and relevant to support digital transformation in the palm oil plantation sector.
Pengaruh Faktor Lingkungan Terhadap Distribusi Kasus DBD di Jakarta Selatan Menggunakan Pendekatan Geographically and Temporally Weighted Regression (GTWR): The Effect of Environmental Factors on the Distribution of Dangue Fever Cases in South Jakarta Using Geographically and Temporally Weighted Regression (GTWR) Approach Carissa, Savvy Prissy Amellia; Sugiarti, Nova Putri Dwi; Trimono, Trimono; Idhom, Mohammad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2116

Abstract

Demam Berdarah Dengue (DBD) adalah penyakit endemis yang dipengaruhi oleh banyak faktor lingkungan dan memiliki pola penyebaran yang kompleks secara spasial dan temporal.  Dengan menggunakan pendekatan Geographically and Temporally Weighted Regression (GTWR), penelitian ini bertujuan untuk menganalisis distribusi kasus DBD di wilayah Jakarta Selatan. Suhu maksimum dan suhu minimum memiliki dampak positif yang konsisten terhadap peningkatan kasus DBD, menurut hasil penelitian.  Kinerja model GTWR ditunjukkan dengan nilai R-squared 0,5697 dan AIC 556,766. Visualisasi peta risiko mengidentifikasi wilayah seperti Jagakarsa, Cilandak, dan Mampang Prapatan sebagai daerah dengan risiko tinggi, dan pola musiman memperlihatkan peningkatan kasus pada awal hingga pertengahan tahun serta penurunan pada musim kemarau.
Implementasi Algoritma LightGBM untuk Prediksi Status Gizi Bayi dan Balita di Desa Doko Kabupaten Kediri Thoriqulhaq, Muhammad; Idhom, Mohammad; Maulida Hindrayani, Kartika
Jurnal Teknik Terapan Vol. 4 No. 2 (2025): Oktober
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

MThe issue of nutritional status among infants and toddlers remains a serious concern in Indonesia, particularly in rural areas. Doko Village was chosen as the research location due to its significant challenges in child health. This study aims to develop a nutritional status prediction model based on the LightGBM algorithm, capable of processing anthropometric data to classify nutritional categories such as "Underweight", "Normal", and "Overweight". Using an 80:20 training-to-testing data ratio, the model achieved 97% accuracy and a 94% F1-score. In addition to building the prediction model, this study also developed an interactive web application using Streamlit, and compared its results with the conventional WHO AnthroPlus method. The results indicate that LightGBM offers advantages in terms of speed, flexibility, and predictive accuracy based on local data.
Gema Gasing Aktif Sebagai Upaya Cegah Stunting Di Desa Wonokerto Khasanah, Ema Isfa'atin; Putri, Deva Amalia Rahma; Kurniawati, Dyah Ayu Listyo; Bajramaya, Dewa Widya; Idhom, Mohammad
Jurnal Sosial & Abdimas Vol. 6 No. 2 (2024): Jurnal Sosial & Abdimas
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jsa.v6i2.1726

Abstract

Sustainable Development Goals (SDGs) adalah target yang harus dicapai oleh Indonesia pada tahun 2030. Oleh karena itu, Pengabdian Masyarakat UPN Veteran Jawa Timur bertujuan untuk membantu desa-desa dalam beradaptasi dengan SDGs. Salah satu tantangan utama yang dihadapi Indonesia saat ini adalah tingginya angka stunting. Di Desa Wonokerto, terdapat 10 balita yang teridentifikasi mengalami stunting, sementara Kecamatan Wonosalam dikategorikan sebagai daerah dengan tingkat stunting yang tinggi pada tahun 2024. Program Gema Gasing Aktif (Gerakan Masyarakat Cegah Astasi Stunting dan Promosi Asi Eksklusif) dirancang untuk menyebarkan pengetahuan mengenai pencegahan stunting. Program ini terdiri dari tiga tahap utama, yaitu mendampingi ibu dan balita saat kegiatan posyandu, melakukan sosialisasi mengenai pencegahan stunting dan promosi asi eksklusif kepada remaja, ibu hamil, kader, dan orang tua sebagai upaya intervensi dini, serta memberikan pelatihan khusus kepada kader posyandu oleh BKKBN Provinsi Jawa Timur. Diharapkan bahwa program ini dapat efektif dalam mencegah stunting di Desa Wonokerto. Penelitian ini menggunakan pendekatan deskriptif dengan metode pengumpulan data melalui wawancara dan observasi.
ANALYSIS AND IMPLEMENTATION OF SENTIMENT SYSTEM ON THE ELECTABILITY OF INDONESIAN PRESIDENTIAL CANDIDATES 2024 USING SUPPORT VECTOR MACHINE METHOD Harahap, Jasmine Avrile Kaniasari; Syaifullah JS, Wahyu; Idhom, Mohammad
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2029

Abstract

Indonesia is a country that implements democracy in choosing presidential candidates through the election process. People have their own views on the presidential candidates they support, and in this digital era, social media is the main platform for people to express their opinions. Public opinion can be positive or negative, public opinion, hate speech, and various other comments that can cause hostility, insults, debates, and disputes. In this study, data modeling using the Support Vector Machine (SVM) method will be evaluated using a confusion matrix. The data used for anies data is 1607 tweets, prabowo data is 1761 tweets, and ganjar data is 1607 tweets with the keywords “anies baswedan”, “prabowo subianto”, and “ganjar pranowo” with the data collection period from November - December 2023. The results of this study show that the sentiment classification model has good performance. For Anies Baswedan data, the SVM model achieved accuracy of 86.64%, precision of 86.69%, recall of 86.64%, and f1-score of 86.62%. For Prabowo Subianto data, the model achieved an accuracy of 90.65%, precision of 90.81%, recall of 90.65%, and f1-score of 90.61%. Meanwhile, for Ganjar Pranowo data, the model achieved an accuracy of 93.78%, precision of 93.67%, recall of 93.78%, and f1-score of 93.72%. These results show that the system is able to classify people's sentiment.
Optimizing Clustering Analysis to Identify High-Potential Markets for Indonesian Tuber Exports Prasetya, Dwi Arman; Sari, Anggraini Puspita; Idhom, Mohammad; Lisanthoni, Angela
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/skzqbd57

Abstract

Agriculture is a key contributor to Indonesia's economic growth, with tubers representing the second most important food crop. Despite their significance, the export value of Indonesia’s tuber crops has not yet reached its full potential given the decline in the value of tuber exports since 2021. One of the contributing factors is the restricted range of export market options. This study aims to analyze export trade patterns to identify the most high-potential markets for Indonesian tuber commodities.  Clustering analysis is used as a key method to identify market locations by grouping countries based on similar trade characteristics. Clustering was conducted using the Gaussian Mixture Model (GMM), which enhanced by Particle Swarm Optimization (PSO) and evaluated by silhouette score and DBI. The dataset is collected from Indonesia’s Central Bureau of Statistics from 2019 to 2023, focusing on 5 kinds of tuber exports with total of 455 entries and 8 columns. Using the AIC/BIC method, the optimal number of clusters obtained is 2 which are low market opportunities (cluster 0) and high market oppurtunities (cluster 1). Results showed that the GMM model without optimization has silhouette score of 0.7602 and DBI of 0.8398, while the GMM+PSO model achieved an improved silhouette score of 0.8884 and DBI of 0.5584. Both score are categorized as strong structure but, GMM+PSO has higher silhouette score and lower DBI score, demonstrating the effectiveness of PSO in enhancing the clustering model’s performance. The key potential markets for Indonesian tuber exports are primarily concentrated in Asia, including countries such as China, Malaysia, Thailand, Vietnam, Hong Kong, and United States.
FORECASTING SALES USING SARIMA MODELS AT THE SINAR PAGI BUILDING MATERIALS STORE Aminullah, Ahmad Adiib; Idhom, Mohammad; Saputra, Wahyu Syaifullah Jauharis
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8266

Abstract

Sinar Pagi Building Materials Store faces the challenge of maintaining optimal stock levels of goods to avoid excess and understock, which affects customer satisfaction and operational efficiency. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to forecast sales in the store. Leveraging its ability to model seasonal patterns on historical sales data, various SARIMA models were analyzed and compared using the Akaike Information Criterion (AIC) and Root Mean Square Error (RMSE). The dataset is divided by a 95:5 ratio into training and testing sets for robust evaluation. The results show that the SARIMA model with SARIMA notation (p,d,q)(P,D,Q  has the best model value of (1,0,0) . This model is the most suitable model based on the lowest AIC value of 1245 and the lowest RMSE of 7,95 compared to other SARIMA models after model identification using the model looping test. For other models such as model (1,0,1)  and (0,0,1) , the AIC and RMSE values are greater, namely model (1,0,1)  with AIC 1246 and RMSE of 8,05, while model (0,0,1)  gets an AIC of 1252 and an AIC of 8,15 .The lower the AIC value, the better the model and the lower the RMSE value, the better the model. This shows a superior balance between model complexity and prediction accuracy. The model manages to capture seasonal patterns in sales data, providing a pretty good prediction framework. This study shows that the SARIMA (1,0,0)  model is effective in the accuracy of the sales forecasting process so that Sinar Pagi Building Materials Store can make more reliable sales predictions, which can help in inventory planning and marketing strategies
Co-Authors Adam, Cindi Akbar , Fawwaz Ali Alfan Rizaldy Pratama Alif, Rahmat Istighfaroni Aminullah, Ahmad Adiib Angga, Angga Rahmad Purnama Anggraini Puspita Sari Anniswa, Iqbal Ramadhan Aviolla Terza Damaliana Azzahra, Adelia Ramadhina Bajramaya, Dewa Widya Basuki Rahmat Masdi Siduppa Cahaya Purtri Agustika Carissa, Savvy Prissy Amellia Damaliana, Aviolla Terza Dewi , Deshinta Arrova Diash, Hakam Dzakwan Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Fahrudin, Tresna Maulana Gede Susrama Mas Diyasa, I Gunawan, Boy Erdyansyah Halim, Rahman Nur Harahap, Jasmine Avrile Kaniasari Henni Endah Wahanani Jauharis Saputra, Wahyu Syaifullah JS, Wahyu Syaifullah Kartika Maulida Hindrayani Khasanah, Ema Isfa'atin Kristiawan, Kiki Yuniar Kurniawati, Dyah Ayu Listyo Kuswardhani , Hajjar Ayu Cahyani Lidya Musaffak, Awal Linggasari, Dienna Eries Lisanthoni, Angela Maulana, Hendra Maulida Hindrayani, Kartika Maulida, Kartika Muhaimin, Amri Muhammad Rizki Alamsyah Muhammad Thoriqulhaq Mutiara Irmadhani Nabila, Nasywa Azzah Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nathania, Vannesa naufal firdaus, ahmad Pamungkas, Syahrul Ardi Panglima, Talitha Fujisai Permadani, Citra Amelia Intan Priananda, Arya Mahardika Putri, Deannisa Syafira Putri, Deva Amalia Rahma Ramadani, Nurmalita Ramadhan Anniswa, Iqbal Raynaldi, Achmad Riyantoko, Prismahardi Aji Ryan Dana, Alvin Saputra, Wahyu Syaifullah Jauharis Shaffa Ameera, Divanda Sugiarti, Nova Putri Dwi Susrama Mas Diyasa, I Gede Syaifullah J. S, Wahyu Syaifullah JS, Wahyu Terza Damaliana, Aviolla Thohir, A. Zaki Thoriqulhaq, Muhammad Trimono Trimono, Trimono Ulayya, Yasmin Wardana, Azel Christian Wardani, Firly Setya Widi Saputro, Tegar