Article Per Year (5 Year)
p-Index From 2021 - 2026
6.715
P-Index
This Author published in this journals
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
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

Published : 79 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search
Journal : bit-Tech

 1 
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.
Development of Blockchain-Based Escrow System with IPFS Protocol for Secure Digital Transactions Pelean Alexander Jonas Sitompul; Henni Endah Wahanani; Achmad Junaidi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3337

Abstract

Digital transactions are essential to modern economic activities, yet challenges related to trust, transparency, and security persist. This research develops a blockchain-based escrow system integrated with the InterPlanetary File System (IPFS) to address these issues through a decentralized, tamper-resistant architecture. The primary aim is to create an escrow platform that minimizes human intervention while ensuring data integrity, thereby overcoming limitations found in traditional escrow mechanisms that rely heavily on legality and banking institutions. This study demonstrates the feasibility of blockchain technology enhancement to existing escrow models, especially for traders conducting high-value digital transactions. The system enables secure interactions between buyers, sellers, and viewers through a decentralized application (dApp) that assigns user roles and executes transaction logic. Funds are securely locked within the smart contract, while digital assets are stored in IPFS. In cases of dispute, the viewer can cancel the transaction, triggering an automated refund to the buyer and deletion of associated asset data to maintain fairness and security. Smart contract development and testing are carried out using the Hardhat framework before deployment to networks such as the Ethereum-based Sepolia Testnet. The results show that the proposed system reduces transaction risks, increases user trust, and enhances transparency throughout the digital transaction process. This research introduces a practical framework for decentralized escrow systems and provides valuable insights for industries seeking secure, blockchain-driven transaction solutions. The system developed in this study serves as a reference model for integrating traditional transaction with blockchain technology, encouraging broader adoption and future exploration of decentralized systems.
Design and Development of an IoT-Based Rain Intensity Prediction System Using LoRa M. Arif; Mohammad Idhom; Henni Endah Wahanani
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
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.
Uncovering Hidden Security Risks in Government Web Portals Using Penetration Testing and Attack Modeling Belia Putri Salsabila; Henni Endah Wahanani; Achmad Junaidi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3776

Abstract

Government web portals that consolidate public services and process personally identifiable data are prime targets for cyber adversaries. However, many existing assessments rely on single-framework methodologies that provide limited adversarial context and insufficient prioritization guidance. This study evaluates the security posture of System X, a public-facing government portal in Indonesia, using a grey-box penetration testing approach that integrates OWASP Top 10:2021, CVSS v3.1, and MITRE ATT&CK. Automated scanning using OWASP ZAP and Nessus identified 12 potential vulnerabilities, which were subsequently validated through manual testing using Burp Suite, cURL, SQLmap, and browser developer tools. The validation process confirmed nine True Positives and three False Positives, resulting in a 25% false positive rate, consistent with prior studies on government web applications. The identified vulnerabilities fall within Broken Access Control, Security Misconfiguration, and Identification and Authentication Failures, with CVSS Base Scores ranging from 4.2 to 6.1. Unlike traditional severity-based assessments, the integration of MITRE ATT&CK enables adversarial behavior mapping and reveals dependency relationships between vulnerabilities. For example, a single Content Security Policy (CSP) misconfiguration was found to enable multiple attack techniques (T1059.007), demonstrating that addressing one root cause can mitigate several related vulnerabilities simultaneously. This integrated approach enhances vulnerability prioritization by providing both severity and attacker-context insights, offering more actionable remediation strategies compared to single-framework methods. The findings contribute to improving practical security assessment methodologies for government systems and support evidence-based cybersecurity decision-making.
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 Az-zahra, Firlie Aurellia Bagaskara, Bregas Arya Bariq Satrio Yudoko Basuki Rahmat Masdi Siduppa Belia Putri Salsabila 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 M. Arif Made Hanindia Prami Swari Mandyartha, Eka Prakarsa Mohamad Ilham Prasetyo Raharjo Mohammad Idhom 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 Pelean Alexander Jonas Sitompul Phitria, Shaum Prakoso, Galih Indo Putra, Chrystia Aji Putra, Chystia Aji Putri, Della Atika Raditya Dimas Libriawan Rahmawati, Aniisah Eka Ramadhaniar, Alfi 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