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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) Prosiding SNITP (Seminar Nasional Inovasi Teknologi Penerbangan) bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) 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 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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Journal : bit-Tech

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Cooking Oil Price Forecasting in East Java Using the Temporal Fusion Transformer Azis, Nauval Ihsani; Sugiarto, Sugiarto; Idhom, Mohammad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3550

Abstract

Cooking oil is a critical staple commodity in Indonesia, where price fluctuations significantly impact household purchasing power and regional economic stability, especially in East Java. These fluctuations stem from complex, nonlinear interactions between crude palm oil prices, supply chain conditions, and market mechanisms. This study fills the gap in existing forecasting models, which often fail to address regional price dynamics and lack interpretability. We develop a short-term forecasting model using the Temporal Fusion Transformer (TFT), a deep learning architecture tailored for multi-horizon time series forecasting, to predict packaged and bulk cooking oil prices in East Java. Daily price data for packaged and bulk cooking oil, along with national palm oil prices, were sourced from official government records covering April 21, 2022, to October 2, 2024. The dataset was preprocessed with missing value interpolation, normalization, and transformation into a supervised multivariate time series format. The TFT model was trained using a 30-day historical window, with a seven-day forecasting horizon, optimized via quantile loss to generate probabilistic forecasts. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and quantile loss. Results show that the TFT model achieves high accuracy, low validation errors, and provides reliable uncertainty estimates. Short-term forecasts suggest stable price trends, with greater uncertainty for packaged cooking oil than bulk. This research demonstrates the TFT's potential for short-term forecasting, policy support, and its broader application to regional price monitoring.
A Deep Learning Approach Using Bidirectional-LSTM and Word2Vec for Fake News Classification Nur Hidayat, Fadhilah; Syaifullah J. S, Wahyu; Idhom, Mohammad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3575

Abstract

The rapid growth of online news consumption in Indonesia has intensified the challenge of combating fake news, which undermines public trust and threatens social stability. Conventional approaches, including manual verification, are increasingly inadequate to address the scale and speed of digital information dissemination. This study aims to develop an automatic Indonesian fake news classification system using a deep learning framework that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) with Word2Vec embeddings. Unlike many existing fake news detection models that rely on limited validation settings or focus predominantly on English-language data, this work explicitly addresses the linguistic characteristics and practical constraints of the Indonesian context, thereby strengthening model relevance for real-world deployment. The dataset comprises 6,000 balanced news articles, including 3,000 valid items from Detik.com and 3,000 hoax items from Turnbackhoax.id, collected between January and October 2024. Text preprocessing involved cleaning, stopword removal, tokenization, and padding. A 300-dimensional Word2Vec embedding model was employed, and the classifier was trained using stratified 3-fold cross-validation to ensure robust performance estimation. An ensemble inference strategy was further applied to reduce inter-fold variance and enhance generalization on unseen data, directly addressing a common limitation of prior single-model approaches. Experimental results show that the proposed model achieves an accuracy of 86.43% and an F1-score of 86.28%, alongside a high mean Average Precision of 0.927 during validation. Compared with previously reported deep learning baselines, this framework demonstrates competitive yet more stable performance under realistic evaluation settings, supporting scalable deployment.
Design and Development of an IoT-Based Rain Intensity Prediction System Using LoRa Arif, M.; Idhom, Mohammad; Wahanani, Henni Endah
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3704

Abstract

An Internet of Things (IoT)–based system for rain intensity monitoring and next-day prediction is presented by integrating low-power wide-area communication using LoRa with cloud-based processing for outdoor and rural environments. This study evaluates the feasibility of LoRa communication and the end-to-end operational reliability of an IoT–cloud pipeline, while positioning machine learning as a supporting decision-aid module. A low-cost sensing node equipped with temperature, humidity, and wind-speed sensors is connected to a LoRa-based gateway that forwards measurements to an Amazon EC2 cloud server via MQTT for centralized storage, processing, and notification delivery. The system is evaluated through a 10-day single-node real-world outdoor deployment, focusing on sensor data acquisition reliability, LoRa link quality, and end-to-end operation from data acquisition to user notifications. The classification module achieves an overall accuracy of 0.74 with a weighted F1-score of 0.71, while minority-class performance remains limited due to class imbalance. LoRa communication remains stable with RSSI values of −80.91 to −79.19 dBm, SNR values of 9.86–9.95 dB, and packet loss rates below 3%. By jointly evaluating LPWAN communication reliability and cloud-side predictive services within a single field deployment, the results demonstrate the practicality of LPWAN-based IoT sensing with cloud integration for rain intensity monitoring in resource-constrained environments, while highlighting the need for future improvements in minority-class prediction and multi-node scalability.
Co-Authors Adam, Cindi Adelia Adelia, Adelia Akbar , Fawwaz Ali Alfan Rizaldy Pratama Alif, Rahmat Istighfaroni Aminullah, Ahmad Adiib Angga, Angga Rahmad Purnama Anggraini Puspita Sari Anniswa, Iqbal Ramadhan Arif, M. Azis, Nauval Ihsani 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 Nabila, Nasywa Azzah Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nathania, Vannesa naufal firdaus, ahmad Nur Hidayat, Fadhilah 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 Sugiarto S 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 Widi Saputro, Tegar