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All Journal Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Ilmiah Media Sisfo Jurnal Riset Informatika bit-Tech Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Teknologi Informatika dan Komputer Nusantara Science and Technology Proceedings Journal of Technology and Informatics (JoTI) International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Kolaboratif Sains Data Sciences Indonesia (DSI) IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Kohesi: Jurnal Sains dan Teknologi Journal of Sustainability, Society, and Eco-Welfare Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam Emerging Statistics and Data Science Journal
Trimono
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Prediksi Penyaluran Obat Kandungan Misoprostol dengan Metode Temporal Convolutional Networks Ramadani, Nurmalita; Idhom, Mohammad; Trimono
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025126

Abstract

Aborsi ilegal di Indonesia masih menjadi permasalahan serius, terutama dengan maraknya penggunaan misoprostol yang diperjualbelikan secara ilegal. Indonesia mencatat sekitar 1,7 juta kasus aborsi per tahun, dengan 42,5 dari setiap 1.000 wanita usia subur di Pulau Jawa terlibat dalam praktik ini. Berdasarkan laporan kasus, penyalahgunaan misoprostol dapat menyebabkan komplikasi serius seperti hipertermia, hipoksia, hingga kematian akibat kegagalan multiorgan. Selain itu, ditemukan bahwa 73% obat aborsi yang dijual online mengandung misoprostol, dan lebih dari 300.000 situs penjual obat ilegal telah diblokir oleh Kementerian Komunikasi dan Informasi. Salah satu celah yang mempermudah penyalahgunaan adalah belum adanya regulasi batas kuantitas penyaluran obat tersebut. Penelitian ini menerapkan model Temporal Convolutional Networks (TCN) untuk memprediksi pola penyaluran obat misoprostol menggunakan data primer dari BPOM dengan periode 2021-2024. Hasil evaluasi menunjukkan bahwa TCN secara konsisten lebih unggul dibandingkan LSTM pada semua panjang input. TCN mencatat rata-rata penurunan NMAE sebesar 85% dan NMSE sebesar 68% dibandingkan LSTM. Pendekatan berbasis TCN ini diharapkan dapat membantu otoritas dalam meningkatkan pengawasan distribusi obat serta mendukung kebijakan pengendalian misoprostol agar tidak disalahgunakan.   Abstract Illegal abortion in Indonesia remains a serious problem, especially with the widespread use of misoprostol, which is sold illegally. Indonesia records around 1.7 million abortion cases per year, with 42.5 out of every 1,000 women of childbearing age on the island of Java involved in this practice. According to case reports, the misuse of misoprostol can lead to serious complications such as hyperthermia, hypoxia, and even death due to multi-organ failure. Additionally, it was found that 73% of abortion drugs sold online contain misoprostol, and over 300,000 illegal drug-selling websites have been blocked by the Ministry of Communication and Information. One loophole that facilitates misuse is the lack of regulations on the quantity of the drug's distribution. This study applied the Temporal Convolutional Networks (TCN) model to predict the distribution patterns of misoprostol using primary data from the Indonesian Food and Drug Administration (BPOM) for the period 2021-2024. Evaluation results show that TCN consistently outperforms LSTM across all input lengths. TCN achieves an average reduction of 85% in NMAE and 68% in NMSE compared to LSTM. This TCN-based approach is expected to assist authorities in enhancing drug distribution oversight and supporting misoprostol control policies to prevent misuse.
CIRCLE: A digital platform for circular food waste management in achieving sustainable food security Auralia, Karina; Dewi, Ni Luh Ayu Nariswari; Witanto, Steffany Marcellia; Trimono
Journal of Sustainability, Society, and Eco-Welfare Vol. 3 No. 2: January (2026)
Publisher : Institute for Advanced Science, Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/jssew.v3i2.2026.2436

Abstract

Background: Food loss and waste (FLW) pose a major global challenge, threatening food security, economic stability, and environmental sustainability. In Indonesia, despite abundant resources, inefficiencies in production and distribution still lead to significant waste and nutritional inequality. Overcoming this issue requires an integrated and sustainable system that improves redistribution efficiency. Supported by digital innovations such as Artificial Intelligence (AI), the Internet of Things (IoT), and data analytics, the circular economy approach offers a strategic solution. In response, the CIRCLE platform was developed as a smart and sustainable digital system for food redistribution. Methods: This study uses a descriptive method through a literature review to identify theories, concepts, and best practices on circular economy, based digital platforms for reducing FLW. Secondary data from scientific publications and institutional reports were analyzed to form the conceptual basis for designing the CIRCLE (Circular Utilization of Food Resources) platform. Findings: The literature emphasizes the importance of multi-stakeholder collaboration and the application of AI, IoT, and data analytics to develop efficient and sustainable food distribution systems. The implementation of user-centered design and gamification is also recommended to enhance user engagement and awareness. Conclusion: The CIRCLE platform represents an innovative and sustainable digital solution to reduce food waste, strengthen food security, and foster collaboration toward achieving SDG 2 and SDG 12 in Indonesia. Novelty/Originality of this article: This study introduces the CIRCLE platform as a distinctive integration of circular economy principles and digital technologies, including AI, IoT, and gamification, within a unified system for reducing food loss and waste in Indonesia.
Implementation of Temporal Fusion Transformer (TFT) for Short-Term Sales Prediction of Telkomsel Data Packages in East Java Muhammad Azkiya Akmal; Trimono; Alfan Rizaldy Pratama
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3268

Abstract

The development of the cellular telecommunications industry has driven an increasing demand for fast, stable, and affordable data services. Accurate forecasting of data package sales is a significant challenge for telecommunications operators due to high demand fluctuations and the complexity of time series patterns. This study aims to implement a Temporal Fusion Transformer (TFT) model based on Seasonal-Trend Decomposition using Loess (STL) to predict short-term sales of Telkomsel data packages in East Java. The data used are sales transactions with hourly time resolution from January to June 2024, focusing on the five data packages with the highest transaction volume. The STL method is applied in the pre-processing stage to separate the trend, seasonal, and residual components, which are then used as additional features in the TFT modeling. Model performance is evaluated using Mean Absolute Error (MAE) and Quantile Risk (q-Risk). The results show that the TFT model is able to produce accurate predictions with an MAE value of 3.6941 and an average q-Risk of 0.0808. Furthermore, interpretability analysis revealed that historical sales variables, seasonal components, and calendar variables significantly contributed to the prediction results. These findings indicate that the STL-based TFT approach is effective for short-term sales forecasting and has the potential to support data-driven operational decision-making in the telecommunications sector.
Co-Authors Abda Abda Afidria, Zulfa Febi Ajeng Puspa Wardani Aji Riyantoko, Prismahardi Alfan Rizaldy Pratama Alzam , Muhammad Arsyad Amri Muhaimin Ananta, Aditya Putra Anggie, Erna Novita Anugrah, Muhammad Cahya Raka Ardiani, Ardia Eva Ardra Jamie Hibatullah Ardra Jamie Hibatullah, Genesis Arfiansyah, Muhammad Nabil Putra Arifta, Septia Dini Aryaputra Jagaddatri Auralia, Karina Aviolla Terza Damaliana Baktiar Putri, Milla Akbarany Bhalqis, Anissa Andiar Cokro, Risbuwono Heru Damaliana, Aviolla Terza Dewi, Ni Luh Ayu Nariswari Diana Novitasari, Diana Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Elmaliyasari, Shifa Farhan Syah Putra Wiyono Fatmala, Friza Nur fernando, Mochamad Firman Fiqih Pavita Andharluana Gestyaki, Jacinda Ardina Hadin, Tiara Audrey Anugerah Hayu, Nahda Hibatulah, Ardra Hidayah, Amellia Harmaimun I Maruddani , Di Asih idhom, Mohammad Iffadah, Adhisa Shilfadianis Irawan, Tanaya Anindita Isyanto, Aisyah Kirana Putri Junior, Nouval Arya Kaffi, Laisal Kamila, Rosyidatul Karnaen, Amelia Zafira Kartika Maulida Hindrayani Khairunisa, Adenda Krisnawan Kristanaya, Mirechelin Kusharyadi, M. Nurhadyatullah Kusharyadi, Muhammad Nurhadyatullah Laksana Aryananda, Rangga Maulana, Mohammad Hikmal Maulidya Prastita Syah Melinda Putri Azzahra Mohammad Idhom Muhaimin, Amri Muhammad Azkiya Akmal Muhammad Nasrudin Najma P., Safira Namira, Alivia Salma Nurdiana, Pinka Ozzari, Nikita Aprilia Pakpahan, Vera Febrianti Pasha, Naufal Ricko Maulana Puti Cresti Ekacitta Putri, Safira Rahmalia rachmanto, Nugroho Fajar Ramadani, Nurmalita Ramadhanti, Amirah Rizky Rhomaningtias, Lina Rizkiyah, Selly Rizqin, Indira Zein Sakhi, Difta Alzena Selayanti, Nabilah Sinulingga, Kevin Brema Saputra Susrama Masdiyasa, I Gede Syaifullah J, Wahyu Syamsiar, Syamsiar Wan Awang, Wan Suryani Westerdam Sean Jatindra, Reagen Witanto, Steffany Marcellia Zufar Abdullah Rabbani