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All Journal Syntax Jurnal Informatika CommIT (Communication & Information Technology) Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer International conference on Information Technology and Business (ICITB) Jurnal Sistem Informasi dan Bisnis Cerdas Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer INTEGER: Journal of Information Technology JIEET (Journal of Information Engineering and Educational Technology) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Informatika dan Rekayasa Elektronik bit-Tech Journal of Appropriate Technology for Community Services JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jurnal Layanan Masyarakat (Journal of Public Service) Jifosi Nusantara Science and Technology Proceedings International Journal Of Computer, Network Security and Information System (IJCONSIST) KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Dan Tekonologi Komputer (JITEK) East Asian Journal of Multidisciplinary Research (EAJMR) Jurnal Teknik Informatika dan Teknologi Informasi Jurnal Krisnadana JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal Pepadu Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Krisnadana Jurnal Informatika Polinema (JIP) Router : Jurnal Teknik Informatika dan Terapan Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Repeater: Publikasi Teknik Informatika dan Jaringan Prosiding Seminar Nasional Ilmu Teknik Router : Jurnal Teknik Informatika dan Terapan Jurnal Informatika Dan Tekonologi Komputer
Henni Endah Wahanani
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LSTM with Attention Optimization for IDR-USD Exchange Rate Forecasting Muhammad Abdullah Hafizh; Anggraini Puspita Sari; Henni Endah Wahanani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3131

Abstract

This study proposes the application of the LSTM-Attention model to forecast the IDR exchange rate against the USD. Exchange rate stability is an important element in national and international economic resilience systems, as currency fluctuations can have a significant impact on trade, investment, banking, and household consumption. In the case of Indonesia, which is highly dependent on imported goods, exchange rate fluctuations cause an increase in import costs, rising inflation, and a decline in the competitiveness of export products in the global market, making accurate forecasting of exchange rate movements essential for economic policy, business strategy, and risk management. Statistical models such as ARIMA have been widely applied in exchange rate forecasting, but they have difficulty capturing the nonlinear of time series data. In recent years, machine learning methods such as Long Short-Term Memory (LSTM) have demonstrated their ability to handle timeseries data. Previous studies have shown that LSTM models generally outperform traditional methods, but they still face limitations in identifying important features across time steps. To overcome this problem, the Attention mechanism allows the model to focus on the most informative parts of the input sequence, thereby improving prediction accuracy. Experimental results show that the LSTM-Attention achieves MAPE of 1.28% and R2 of 0.97 and runtime 45% faster than BiLSTM. While BiLSTM achieved slightly higher accuracy, it’s required nearly twice the training time. Findings indicates that the proposed model offers practical choice for real-time exchange rate forecasting.
Fuzzy C-Means Clustering of Regencies and Cities Based on Total Sanitation Society Ananda Azra Razali; Eva Yulia Puspaningrum; Henni Endah Wahanani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3180

Abstract

The Community Based Total Sanitation (STBM) program is a national initiative designed to enhance public health by promoting clean and healthy living habits. However, its implementation in several regions, including East Java Province, continues to encounter a number of challenges, as several sanitation indicators have yet to reach the desired targets. This study aims to group the sanitation performance of regencies and cities in East Java using the Fuzzy C Means (FCM) algorithm and visualize the outcomes through thematic maps to provide clearer and more informative spatial insights. Six key indicators. Six key indicators CTPS, PAMMRT, PSRT, PLCRT, PKURT, and Healthy Home Access were analyzed as percentages, with variable selection and normalization conducted using the Min Max Scaler to ensure comparable value ranges across datasets. The clustering validity was assessed using the Davies Bouldin Index (DBI), where the lowest value of 0.9134 was achieved for three clusters, indicating the most optimal grouping configuration. The resulting clusters represent regions with high, medium, and low sanitation achievement levels, while spatial visualization reveals that lower-performing regions are largely concentrated in the eastern part and the Madura area. From a practical standpoint, the findings of this study can serve as a foundation for policy formulation, intervention prioritization, and more efficient resource allocation to improve regional sanitation performance in a focused and sustainable manner.
Implementation of Facebook Prophet Algorithm in Population Prediction Raditya Dimas Libriawan; Anggraini Puspitasari Sari; Henni Endah Wahanani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3190

Abstract

The number of populations in a country is a very important aspect because it has a direct effect on various aspects of life. Indonesia is in the fourth position of the country with the largest population in the world. It is recorded in the Indonesian Central Statistics Agency (BPS) that by mid-2024, the population in Indonesia will reach 281.603.800 people. The ever-increasing population will drive increased energy demand. Therefore, monitoring and controlling population growth is a crucial and indispensable step, one of which is by utilizing machine learning to conduct time series forecasting. This study contributes by optimizing FB Prophet’s parameter configuration for population forecasting in Indonesia, achieving improved accuracy compared to traditional models. The purpose of this study is to determine the level of accuracy and error of the model with evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results obtained from forecasting using the Prophet algorithm were that Indonesia increased by 1.5% by the end of 2025, with the value of the MAE evaluation metric of 0.0244, RMSE of 0.0256, and MAPE of 2.65%, which indicates a highly accurate prediction level for annual population data.
Optimizing the ResNet50 Model with Five Optimizers for Detecting Rice Leaf Diseases Muchammad Syamsu Huda; Henni Endah Wahanani; Fetty Tri Anggraeny
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3232

Abstract

Rice (Oryza sativa) productivity is frequently threatened by foliar diseases such as Bacterial Leaf Blight, Brown Spot, Blast, and Tungro, which are often visually indistinguishable. This study achieved a high classification accuracy of 97.05% in detecting these diseases by optimizing the ResNet50 architecture with five optimizers Adam, Nadam, Adamax, RMSprop, and SGD and identifying Adamax as the most effective. Using transfer learning with ImageNet weights and data augmentation, the model was trained and validated on 4,400 labeled images from Kaggle, partitioned in a 70:20:10 ratio for training, validation, and testing. The methodological framework integrates three layers of innovation: (1) optimizing a deep residual CNN with comparative adaptive and non-adaptive optimizers; (2) employing transfer learning to accelerate convergence and reduce overfitting; and (3) deploying the best-performing model into an Android-based mobile application for real-time field detection. Results demonstrate that adaptive optimizers substantially enhance ResNet50’s learning stability and generalization compared to traditional methods. The Adamax variant exhibited the most stable convergence and minimal validation loss, proving effective for fine-grained visual differentiation between similar disease patterns. This research advances the current state-of-the-art in agricultural image classification by providing a systematic optimizer evaluation within a CNN transfer learning framework and extending its practical usability through mobile deployment. Future studies should address model compression, real-time inference optimization, and cross-crop generalization to strengthen the scalability of AI-assisted disease diagnosis in precision agriculture.
Co-Authors Abdi, Harris Cipta Abiyan Naufal Hilmi Achmad Junaidi Aditia Mieka Darminta Adityawati, Dewi Affro, Salma Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Agussalim Agussalim Agussalim, Agussalim Akbar, Fawwaz Ali Al Afgany, Muhammad Iqbal Al Hamda, Veqqy Ananda Azra Razali Andreas Nugroho Sihananto Anggraini Puspita Sari Anggraini Puspitasari Sari Ani Dijah Rahajoe Aniisah Eka Rahmawati Arif Saifudin, Muhamad Arimawan, Kesya Sakha Nesya Arrosyid, Muhammad Habib Arum Prabowo, Galih Bagaskara, Bregas Arya Bariq Satrio Yudoko Basuki Rahmat Masdi Siduppa Budi Mukhamad Mulyo Budi Nugroho Budianto Budianto Chystia Aji Putra Darminta, Aditia Mieka Eka Zuni Selviana Endang Sholihatin Erlangga Wicaksono, Dewa Erlina Diah Karisma Eva Yulia Puspaningrum Fadhilasari, Annisa Fetty Tri Anggraeny Fikri Dwilaksono Firza Prima Aditiawan Fitriansyah, Muhammad Daffa Hamzah Dimas Syah Reza Hermawan, Oky I Made Suartana I Nyoman Sujana idhom, Mohammad IMANDAYANTI, NUR EZA Intan Yuniar Purbasari inthan anggraini, dieas Islah Rachmawati Kristiawan, Kiki Yuniar Lina Nurlaili, Afina Made Hanindia Prami Swari Mandyartha, Eka Prakarsa Mohamad Ilham Prasetyo Raharjo Mohammad Idhom Muchammad Syamsu Huda Muhammad Abdullah Hafizh Muhammad Idhom Muhammad Rizki Alamsyah Muhammad, rizal Muttaqin, Faisal Nafa Nabila El Indri naufal firdaus, ahmad Nugroho, Budi Nugroho, Budi Nugroho, Budi Nur Firmansyah, Taufik Nurlaili, Afina Lina Phitria, Shaum Prakoso, Galih Indo Putra, Chrystia Aji Putra, Chystia Aji Putri, Della Atika Raditya Dimas Libriawan Rahmawati, Aniisah Eka Rayhan Rizal Mahendra Retno Mumpuni Retno Mumpuni Rhiziqo Adjie Syahputra Sandy Rizkyando Sandy, Aditya Noor Saputra, Wahyu S.J. Saputro, Fajar Arif Eko Shabika Aqmarina, Azzuraa Soedarto, Teguh Suartana, I Made Sugiarto Sugiarto - SUGIARTO - Sukirmiyadi, Sukirmiyadi Swasti, Ika Korika TATI NURHAYATI Thohir, A. Zaki Thomas Andrew Imanzaghi Vita Via, Yisti Wahono, Bari Hade Variant Yudha Asmara, I Wayan Zaim, Mohammad Syarifuz