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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUITA : Jurnal Informatika Jurnal Inspiration JURNAL MEDIA INFORMATIKA BUDIDARMA METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JSAI (Journal Scientific and Applied Informatics) Buletin Ilmiah Sarjana Teknik Elektro Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jutech: Jurnal Teknologi Informasi Jurnal Pengabdian Pada Masyarakat METHABDI Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia
Deny Nugroho Triwibowo
Universitas Harapan Bangsa
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Public Health Social Sciences Other

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Lightweight Hybrid Linformer-Mamba U-Net for Efficient Retinal Microaneurysm Segmentation Arif Setia Sandi Ariyanto; Deny Nugroho Triwibowo; Agriby Diandra Chaniago; Indah Trivilia; Annastasya Nabila Elsa Wulandari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31598

Abstract

Diabetic retinopathy is a major microvascular complication of diabetes and a leading cause of vision loss among the working-age population. Microaneurysms (MAs), as the earliest clinical indicators of disease progression, remain challenging to segment due to their small size, low contrast, and extreme class imbalance. This study proposes a lightweight hybrid U-Net architecture for microaneurysm segmentation in retinal fundus images, designed to balance detection sensitivity and computational efficiency for deployment in resource-constrained environments. The proposed architecture integrates depthwise separable convolutions for efficient local feature extraction, a Transformer-Lite bottleneck based on Linformer self-attention for global contextual modeling, and a Mamba State Space Model (SSM)–based decoder to enhance feature propagation and spatial continuity.  The research contribution of this work is threefold: the introduction of an efficient hybrid U-Net combining Linformer and Mamba SSM for microaneurysm segmentation; a deployment-oriented evaluation protocol that explicitly distinguishes patch-level learning behavior from full-image reconstruction performance; and a transparent analysis of false positive behavior under extreme background dominance.  Experiments were conducted on the IDRiD dataset, consisting of 81 retinal images, using patient-level data splitting prior to patch extraction to prevent data leakage.  The results indicate that while patch-level evaluation demonstrates effective lesion-centric learning, deployment-realistic full-image evaluation reveals a notable performance degradation caused by false positive accumulation in extensive background regions. Nevertheless, the model maintains high recall, indicating preserved lesion sensitivity. These findings suggest that lightweight architectural design can deliver meaningful performance and is well suited for screening-oriented decision-support systems that prioritize efficiency and sensitivity.
Perancangan Aplikasi Mobile Berbasis Flutter untuk Pemantauan Data Sensor IoT: Solusi bagi Manajemen Suhu dan Kelembapan Arif Setia Sandi Ariyanto; Iis Setiawan Mangku Negara; Deny Nugroho Triwibowo; Yanuar Feriyanto
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7724

Abstract

This study developed a mobile application based on Flutter to monitor Internet of Things (IoT) sensor data for real-time measurement of temperature and humidity. The system utilizes a DHT22 sensor connected to an ESP32 microcontroller, transmitting data via the MQTT protocol. The data is stored in a MySQL database hosted on a designated server. The mobile application offers an interactive interface to display real-time data, historical graphs, and automatic notifications when environmental parameters exceed predefined thresholds. Testing was conducted using the User Acceptance Testing (UAT) method involving 20 respondents. The results indicate a high level of application success, with an average user satisfaction rate of 93% for ease of login, 90% for the interface, 90% for data processing speed, and 93% for notification accuracy. By integrating IoT, Flutter, and MySQL technologies, this application provides an efficient digital solution for environmental management, with potential for development across various industrial sectors requiring such capabilities.
Implementasi dan Evaluasi Kinerja Sistem IoT Multi-Sensor Berbasis ESP32 untuk Pemantauan dan Peringatan Dini Lingkungan secara Real-Time Arif Setia Sandi Ariyanto; Deny Nugroho Triwibowo; Imam Ahmad Ashari; Rito Cipta Sigitta Haryono
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9861

Abstract

Real-time environmental monitoring has become increasingly important due to growing urban and industrial activities that affect air quality, noise levels, and physical environmental stability. However, many existing monitoring systems remain relatively expensive, lack portability, and are limited to passive monitoring functions without clear performance evaluation. This study aims to implement and evaluate the performance of an Internet of Things (IoT)-based multi-sensor environmental monitoring system integrated with a mobile application and real-time early warning features. The system is developed using an ESP32 microcontroller connected to DHT22, MQ135, SW-420, and KY-037 sensors to monitor temperature, humidity, air quality, vibration, and noise levels. Sensor data are transmitted to a server via a RESTful API, stored in a MySQL database, and visualized in real time through a Flutter-based mobile application. The research adopts a Research and Development (R&D) approach, encompassing requirement analysis, system design, implementation, integration, and functional testing. The experimental results indicate that the system can transmit multi-sensor data reliably with low response time, present environmental information in real time, and consistently deliver early warning notifications when environmental parameters exceed the defined threshold values. This study contributes by providing a practical and replicable performance evaluation of an IoT-based multi-sensor system suitable for small-scale environmental monitoring.
Reproducible Biomedical NER and Proxy Relation Extraction for Drug–Adverse Event Analysis in Breast Cancer Deny Nugroho Triwibowo; Hadi Jayusman; Rachman Hidayat; Anisya; Annastasya Nabila Elsa Wulandari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 12 No. 1 (2026): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v12i1.31594

Abstract

Pharmacovigilance requires automated systems to extract biomedical entities and their relationships from text, as manual processes are inefficient and prone to error. This study develops a reproducible pipeline for Named Entity Recognition (NER) and pattern-based proxy relation formation, focusing on drug side effects related to breast cancer. The research contribution is twofold: a domain-specific annotated dataset for pharmacovigilance NER, and a reproducible pipeline for proxy-based relation analysis. The experimental setup combines MobileBERT, DistilBERT, TinyBERT, and ALBERT. Evaluation is conducted using accuracy, precision, recall, F1-score, ROC AUC, and computational efficiency metrics. The results show that ALBERT achieves the highest NER performance (F1-score = 0.9261), while DistilBERT attains the best ROC AUC (0.9037). TinyBERT is the most efficient model, with 4.57 million parameters, 4.68 G FLOPs, and an average training time of 45.8 seconds per scenario. The proposed pipeline demonstrates a trade-off between accuracy and computational efficiency under the evaluated setting. The generated relations act as sentence-level proxy indicators of potential drug–adverse event associations and serve as a preliminary triage layer requiring expert validation rather than a high-precision system. However, the approach does not account for negation, uncertainty, or cross-sentence context, which may introduce false positive associations. Despite these limitations, the pipeline provides a reproducible baseline for exploratory pharmacovigilance analysis.
Bridging the Gap: Implementasi Aplikasi Portofolio OBE untuk Evaluasi Pembelajaran yang Lebih Efektif dan Efisien Imam Ahmad Ashari; Retno Agus Setiawan; Arif Setia Sandi A.; Deny Nugroho Triwibowo; Anggit Wirasto; Iis Setiawan Mangkunegara; Sony Kartika Wibisono
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 6 No 1 (2026): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol6No1.pp50-56

Abstract

The implementation of the Outcome-Based Education (OBE) curriculum is often hindered by repetitive manual administration. This community service aims to facilitate digital transformation through SIMPOBE application assistance for 39 lecturers at Universitas Harapan Bangsa. The method involved a hands-on workshop covering assessment alignment, RPS standardization, and PPEPP-based raw score input. Evaluation results show the application successfully bridged the gap between curriculum data and evaluation reports. Efficiency significantly improved as the system automatically links assessment components to CPL/CPMK, eliminating redundant input. Most participants (scores 4 and 5) expressed readiness for independent implementation by April 2026. In conclusion, portfolio digitalization effectively reduces human error and administrative burdens, supporting accountable institutional academic quality enhancement.
Pendampingan Ibu Balita Melalui Model Edukasi Gizi Berbasis Aplikasi Digital untuk Pencegahan Stunting di Kabupaten Brebes Arif Setia Sandi A.; Deny Nugroho Triwibowo; Azhar Bashir; Sony Kartika Wibisono; Fitri Ayuningtyas; Iis Setiawan Mangku Negara
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 5 No 2 (2025): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol5No2.pp266-271

Abstract

Stunting is a chronic nutritional problem requiring sustainable intervention, particularly in priority areas such as Brebes Regency. Conventional nutritional interventions often lack sustainability in monitoring child growth and development. This program aims to evaluate the effectiveness of the Digital Application-Based Nutrition Education Model (Si Penting)—a collaboration between Universitas Harapan Bangsa and Universitas Muhammadiyah Brebes—in strengthening caregiver capacity. The activity was conducted in Bantarkawung Sub-district, involving 45 caregivers of toddlers aged 6–24 months. A one-group pre-test post-test design was employed. The intervention consisted of face-to-face nutrition education followed by training and mentoring on the Si Penting Digital Application for two weeks. This program emphasized intensive mentoring for caregivers to operate the application for independent monitoring. Results showed a significant increase in participants' knowledge and attitudes (p<0.05), followed by proactive behavioral changes in monitoring child growth. The intervention model was successful, as shown by a 95.6% adoption rate for the application. It is concluded that the Digital Application-Based Nutrition Education Model effectively helped caregivers improve their ability to monitor nutrition on their own and is suggested to be used alongside Posyandu services to create lasting benefits in lowering stunting risks.
Integrasi Teknologi IoT dan Aplikasi Telegram Untuk Pemantauan Kadar Gula Darah Penderita Diabetes: Membuat sebuah alat pengecekaan kadar gula darah secara non-invasive Jessa Syah Putra; Deny Nugroho Triwibowo; Rian Ardianto
Jurnal Teknologi Informasi (JUTECH) Vol. 6 No. 2 (2025): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v6i2.3191

Abstract

Blood sugar (glucose) is the body's main source of energy and is classified as a monosaccharide. Blood sugar levels are divided into low, normal, and high, with high levels potentially triggering diabetes, one of the main health issues in Indonesia. This research proposes an Internet of Things (IoT)-based health monitoring system to measure blood sugar levels using a photodiode sensor and a red LED as the light source. The system is equipped with real-time notifications via Telegram and data display on an OLED screen. The method used is a prototype with accuracy testing against a glucometer as a comparison tool. The test results showed an average error of 5.28% out of a total error of 105.77%. However, the device often displays the same measurement results repeatedly and shows a “finger not detected” notification due to the sensor's sensitivity to surrounding light.
Klasifikasi Status Gizi Balita Menggunakan Algoritma Support Vector Machine dengan Optimasi Grid Search Cross-Validation Azkiyatun Nadroh; Deny Nugroho Triwibowo; R. Bagus Bambang Sumantri
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp250-257

Abstract

Toddlers are children aged 0 to 59 months who experience rapid growth and development and require a higher intake of nutrients. This study aims to classify the nutritional status of toddlers using the Support Vector Machine (SVM) algorithm with Grid Search optimization. The quality of a toddler's nutrition significantly affects their growth and development, and malnutrition is a major issue in Indonesia. Data were obtained from Posyandu Desa Jagalempeni, comprising a total of 512 toddler data entries. After undergoing pre-processing and feature engineering, the data were classified using SVM. The initial results showed an accuracy of 80%. Following the application of Grid Search optimization with the Radial Basis Function (RBF) kernel, accuracy increased to 86.17%. These results indicate that Grid Search is effective in optimizing SVM model parameters and improving classification performance.
Klasifikasi Jenis Sampah Berbasis Convolutional Neural Network dengan Optimasi Hyperparameter Tuning Arsitektur Mobilenet Dimas Febri Kuncoro; Anggit Wirasto; Deny Nugroho Triwibowo
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp130-144

Abstract

Waste management in Indonesia faces significant challenges with an increasing volume reaching approximately 175,000 tons per day. Public awareness of the dangers associated with improper waste disposal remains low, as many continue to litter indiscriminately. Waste sorting is the most effective method, involving separation based on waste types. Manual waste sorting is nonetheless inefficient, as it requires large spaces, substantial labor, and is prone to errors. This study aims to develop a waste classification model based on Convolutional Neural Network (CNN) with hyperparameter tuning optimization for the MobileNet architecture. The research adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and utilizes datasets from three waste categories organic, inorganic, and hazardous and toxic materials (B3) sourced from open Kaggle datasets. Model training was conducted using the MobileNet architecture with hyperparameter tuning optimization and resulting in optimal parameters Adam optimizer, learning rate of 0.01, batch size of 32, and 256 neurons. The results show that the model achieved 96% accuracy before optimization which increased by 2% to 98% after optimization. The model demonstrated high computational efficiency with the number of floating-point operations per second reaching 1.146 GFLOPS.
Integrating User Acceptance Evaluation into District-Level Mobile Health System Design for Maternal Care Arif Setia Sandi Ariyanto; Purwono Purwono; Deny Nugroho Triwibowo
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29616

Abstract

Many mobile health information systems are developed based primarily on technical requirements, while user acceptance is often assessed only after deployment and rarely integrated into the design process. This study addresses that gap by incorporating user acceptance evaluation as a design-informed feedback mechanism in the development of a district-level mobile health information system for maternal care. An applied research approach was used through system development, pilot deployment across five sub-districts, and post-use evaluation involving 74 active users. User acceptance was assessed using structured questionnaires covering perceived usefulness, perceived ease of use, consultation feature acceptance, and automated conversational support acceptance. The results showed high overall acceptance, with direct consultation with local midwives receiving the highest score (mean = 4.41; SD = 0.52), followed by consultation routing effectiveness (mean = 4.35; SD = 0.55). Perceived ease of use was positively associated with consultation feature acceptance (ρ = 0.46, p < 0.01). Automated conversational support was positively perceived but obtained lower scores (mean = 3.92) than human-based consultation features, indicating its complementary role. These findings demonstrate that user acceptance evaluation can provide actionable evidence for iterative system refinement and support the development of context-aware mobile health systems for maternal care.
Co-Authors Agriby Diandra Chaniago Ahmad Kurniadi Anisya Annastasya Nabila Elsa Wulandari Arif Setia Sandi A. Azhar Bashir Azkiyatun Nadroh Bambang Sumantri, R Bagus Dimas Febri Kuncoro Dony Ariyus Ema Utami Fitri Ayuningtyas Hadi Jayusman Iis Setiawan Mangkunegara Imam Ahmad Ashari, Imam Ahmad Indah Trivilia Irfan Arfianto Jessa Syah Putra Mangku Negara, Iis Setiawan Mochammad Hasymi Somaida Pradestya Bima Arweina Purwono, Purwono Rachman Hidayat Retno Agus Setiawan Rian Ardianto Riska Suryani Rito Cipta Sigitta Haryono Siti Hartinah Sony Kartika Wibisono Sukoco Sukoco Widadi, Budi Willy Setiawan Wirasto, Anggit Yanuar Feriyanto Yusuf Fadlila Rahman