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All Journal Jurnal Simetris Fountain of Informatics Journal Sistemasi: Jurnal Sistem Informasi JURNAL ILMIAH INFORMATIKA RESEARCH : Computer, Information System & Technology Management JUSIM (Jurnal Sistem Informasi Musirawas) Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Tecnoscienza Scientific Journal of Informatics Journal of Innovation Information Technology and Application (JINITA) JiTEKH (Jurnal Ilmiah Teknologi Harapan) DEVICE Bulletin of Information Technology (BIT) Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat The Indonesian Journal of Computer Science Research Biner : Jurnal Ilmiah Informatika dan Komputer Management of Information System Journal
Nugroho, Arie
Prodi Sistem Informasi Universitas Nusantara PGRI Kediri
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Other

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A Local Government Application Capability Level Information System Audit using COBIT 5 Framework Bagus Dwi Andika; Sucipto Sucipto; Arie Nugroho
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.1971

Abstract

The ASN application stores State Civil Apparatus and Employee Work Target master data. ASN application has never been audited. This study aimed to measure the capability level of the ASN application using the COBIT 5 framework. The audit results contain current findings and expectations for the future, then analyze the gaps and make recommendations for improvement. Audit results based on domains DSS01, DSS02, DSS03, DSS04, DSS05, and DSS06 achieve capability level 1 performance process. The ASN application manager has successfully implemented a process that has achieved its goals by finding evidence of work product output. To achieve the expected level, namely level 2 managed process, it is recommended that you complete incomplete output documents and carry out activities that have not been carried out per COBIT 5.
IMPLEMENTASI AUGMENTASI DATA PADA INTEGRASI YOLOV8 DAN DETR UNTUK DETEKSI SEL DARAH PUTIH Ahmad Tohari; Arie Nugroho; Anita Sari Wardani
Device Vol. 16 No. 1 (2026): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v16i1.11317

Abstract

Identifikasi sel darah putih secara konvensional di laboratorium memiliki keterbatasan dalam hal efisiensi waktu dan konsistensi akurasi akibat kelelahan visual manusia. Penelitian ini bertujuan mengimplementasikan arsitektur Detection Transformer (DETR) yang diintegrasikan dengan kerangka kerja YOLOv8 untuk meningkatkan performa deteksi pada lima jenis sel darah putih (Basofil, Eosinofil, Limfosit, Monosit, dan Neutrofil). Fokus utama penelitian adalah mengatasi limitasi arsitektur Convolutional Neural Network (CNN) dalam mendeteksi objek yang saling tumpang tindih (overlapping) serta menyeimbangkan distribusi kelas pada dataset publik yang terbatas. Menggunakan metodologi CRISP-DM, tahap persiapan data menerapkan teknik augmentasi spasial dan oversampling untuk memperkaya 136 citra mentah menjadi 310 citra latih beresolusi 640x640 piksel. Eksperimen dilakukan menggunakan model RT-DETR-L yang memiliki kemampuan Global Context Mapping. Hasil evaluasi menunjukkan performa unggul dengan nilai Presisi 0,957, Recall 0,933, dan mAP50 sebesar 0,931. Model berhasil mencapai akurasi sempurna (1,00) pada klasifikasi Eosinofil, Limfosit, dan Monosit. Kesimpulannya, integrasi arsitektur Transformer dan strategi augmentasi data terbukti sangat efektif dalam meningkatkan akurasi deteksi dan meminimalkan misklasifikasi, menjadikannya solusi yang tangguh untuk diimplementasikan dalam sistem diagnostik medis otomatis berbasis web menggunakan Streamlit.
The Analisa Pengaruh Kepuasan Pengguna Website Talipodo Golden Theatre Menggunakan Metode Webqual 4.0 Reka Ainul Khasanah; Sucipto; Arie Nugroho
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.1987

Abstract

This study aims to assess the quality of the Golden Theatre website based on user satisfaction using the WebQual 4.0 method. The research focuses on three main dimensions: usability, information quality, and service interaction quality. A quantitative descriptive approach was employed through a survey of 100 active users of the Golden Theatre website. Data were analyzed using Prais-Winsten regression and classical assumption tests, processed with Python programming language. The results indicate that among the three variables, only information quality has a significant influence on user satisfaction. Meanwhile, usability and service interaction quality do not have a significant partial effect. Simultaneously, the three variables have a significant impact on user satisfaction. The WebQual Index (WQI) score of 57.19% suggests that the website’s overall quality is moderate but still falls short of user expectations. Therefore, it is recommended that the Golden Theatre website management enhance the quality of information provided, in order to improve service quality and overall user satisfaction.
Optimasi Model Yolov8n Menggunakan Augmentasi Data Untuk Peningkatan Akurasi Sistem Dress-Code Surveillance Sherla Mutia; Rina Firliana; Arie Nugroho
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2760

Abstract

Manual surveillance of student dress-code compliance on campus is often inefficient, subjective, and constrained by the physical fatigue of security personnel. This study aims to automate the surveillance system by optimizing the Nano variant of the YOLOv8 (YOLOv8n) Deep Learning model based on Computer Vision. The main challenge in real-time object detection is limited datasets and visual diversity, which increases the risk of overfitting. The solution applied to address this issue is the implementation of comprehensive dynamic data augmentation strategies, including Hue, Saturation, Value (HSV) manipulation, Horizontal Flipping, and Mosaic Augmentation. Utilizing the CRISP-DM methodology, this technique expanded the dataset from 5,000 initial images to 9,742 training images. The empirical test results show that the optimized YOLOv8n model significantly improved accuracy by 43,5% compared to the baseline model. The best-performing model achieved a Mean Average Precision (mAP@0.5) of 95.3%, with a Precision of 93.1%, Recall of 91.1%, and an F1-score of 0.92. These metrics demonstrate the reliability of the system in reducing false positives while operating in crowded real-world environments. This automated surveillance system is highly feasible for direct integration into campus CCTV infrastructure using edge computing to objectively support institutional discipline.
SISTEM SKORING WOODBALL BERBASIS WEB DENGAN PERHITUNGAN OTOMATIS MENGGUNAKAN METODE WATERFALL Nurun Nihayatur Rifqiyah Aulia; Arie Nugroho; Anita Sari Wardani
Jurnal Ilmiah Informatika Vol. 11 No. 1 (2026): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/.v11i1.9557

Abstract

The score recording process in woodball competitions is still commonly carried out manually using paper sheets, causing recording errors, delays in score recapitulation, and difficulties in managing match data. This study aims to develop a web-based woodball scoring system with automatic calculation to improve the accuracy and efficiency of score management during competitions. The system was developed using the Waterfall method and implemented using PHP, MySQL, HTML, CSS, JavaScript, and Bootstrap. The application provides features such as player management, match management, real-time score input, automatic score calculation, ranking generation, and match result reporting. Based on Black Box Testing results, all system features functioned properly according to user requirements. The developed system is able to reduce manual calculation errors, accelerate score recapitulation, and support real-time monitoring of match results. Therefore, the system can be used as an effective digital solution for woodball match scoring and management.
Analisis Sentimen Ulasan Aplikasi Mobile Legends Berbasis Pelabelan IndoBERT dan SMOTE dengan Komparasi Algoritma Klasifikasi Naive Bayes dan SVM Muhammad Fauzan Aditiya Mufid; Erna Daniati; Arie Nugroho
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2783

Abstract

This study was conducted to evaluate user sentiment trends toward the Mobile Legends: Bang Bang game app through reviews published on the Google Play Store platform. This research applied IndoBERT-based automatic labeling to generate sentiment labels that better represent the textual meaning of the reviews. The class distribution imbalance resulting from the labeling process was addressed using the Synthetic Minority Oversampling Technique (SMOTE) during the model training phase. The performance of two classification algorithms—the Naive Bayes Classifier and the Support Vector Machine (SVM)—was compared through hyperparameter tuning using GridSearchCV, with the F1-Macro metric. The results show that the Naive Bayes Classifier model delivers the best performance with an accuracy of 87.55%, an F1-Score of 85.78%, and an F1-Score of 56.70%. However, the model still exhibits significant limitations in recognizing the neutral sentiment class (F1-Score 0.21), which was further analyzed and found to be caused by the very small proportion of the neutral class (2.3% of the total data) as well as the characteristic of neutral reviews, which tend to be requests or suggestions to developers rather than explicit statements. This study contributes to the development of a more representative sentiment analysis methodology through a combination of transformer-based labeling, data imbalance handling, and classification algorithm comparison.
Sistem Deteksi Jatuh Lansia Real-Time Berbasis YOLOv8 dengan Notifikasi Telegram dan Dashboard Web Claudio Syanu Mareta Dinata; Rina Firliana; Arie Nugroho
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2885

Abstract

Falls in the elderly represent a serious global health problem. According to the WHO, one in three elderly people aged over 65 years experiences a fall each year. In Panti Werda Kediri, monitoring of elderly activities is still performed manually by staff, causing fall incidents to go undetected quickly. Previous YOLO-based fall detection studies generally produce models without integrating them into monitoring platforms usable by non-technical end users and without automatic notification. This research aims to determine the effectiveness of a monitoring system in detecting normal activities and fall incidents in elderly residents in real time at Panti Werda Kediri using YOLOv8. The system was developed using the Waterfall method through stages of requirements analysis, system design, implementation, and testing. The detection component uses a retrained YOLOv8 model to recognize two classes: normal and fall. The backend is built with FastAPI and PostgreSQL, equipped with a web-based monitoring dashboard and automatic notifications via Telegram Bot. A fall confirmation mechanism based on 3 consecutive frames with a 1.5-second cooldown suppresses false positives. Blackbox testing conducted at Panti Werda Kediri shows all 10 test scenarios passed. The system successfully sends real-time Telegram notifications in under 2 seconds with visual evidence each time a fall is confirmed, provides live camera streaming, and displays complete detection history through a web dashboard accessible to non-technical staff.
Pelatihan Edukasi Cyberbullying dan Pemanfaatan Artificial Intelligence (AI) melalui Pembuatan Konten Digital Menggunakan Canva pada Posyandu Remaja Desa Kedawung Arie Nugroho; Achmad Nabila Abas; Anindya Purbasari; Kevin Satrio Cahyono; Moh. Azka Wildan; Shanti Oktafia; Aidina Ristyawan; Muhammad Najibulloh Muzaki
Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Vol. 7 No. 1 (2026): November 2026
Publisher : Cipta Media Harmoni

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53624/kontribusi.v7i1.959

Abstract

Latar Belakang: Penggunaan media sosial di kalangan remaja memberikan manfaat, tetapi juga meningkatkan risiko cyberbullying. Di sisi lain, Artificial Intelligence (AI) dapat dimanfaatkan untuk menghasilkan konten digital yang kreatif dan edukatif. Tujuan : Kegiatan ini bertujuan meningkatkan pemahaman remaja tentang cyberbullying, etika bermedia sosial, serta keterampilan menggunakan Canva AI. Metode : Metode yang digunakan adalah edukasi partisipatif melalui empat tahapan, yaitu identifikasi kebutuhan, edukasi dan simulasi cyberbullying, pelatihan Canva AI, serta evaluasi dan presentasi hasil. Peserta kegiatan berjumlah 29 anggota Posyandu Remaja Desa Kedawung. Hasil : Hasil kegiatan menunjukkan peningkatan pemahaman peserta mengenai bentuk, dampak, dan pencegahan cyberbullying serta kemampuan menggunakan Canva AI untuk membuat poster digital edukatif. Kesimpulan : Kegiatan ini berhasil meningkatkan literasi digital dan keterampilan peserta dalam menghasilkan konten positif untuk mendukung penggunaan media sosial yang bijak dan bertanggung jawab.
Perbandingan Kinerja Algoritma SVM, LSTM, dan Fine-tuned IndoBERT dalam Analisis Sentimen Opini Masyarakat Indonesia terhadap Mobil Listrik Erna Daniati; Arie Nugroho; Aidina Ristyawan; Hastari Utama
The Indonesian Journal of Computer Science Research Vol. 5 No. 1 (2026): Januari
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i1.245

Abstract

Penelitian ini menyajikan analisis sentimen terhadap opini publik di Indonesia mengenai mobil listrik menggunakan pendekatan fine-tuning pada model IndoBERT untuk meningkatkan akurasi klasifikasi sentimen. Dengan semakin meningkatnya pergeseran global menuju transportasi berkelanjutan, memahami persepsi masyarakat sangat penting bagi keberhasilan adopsi mobil listrik di Indonesia. Penelitian ini menggunakan dataset berisi 1.517 komentar berbahasa Indonesia yang dikumpulkan dari platform media sosial dan dilabeli menjadi tiga kategori sentimen: positif, negatif, dan netral. Model yang digunakan adalah IndoBERT-base yang diperbaiki melalui proses fine-tuning pada dataset tersebut untuk meningkatkan performanya dalam klasifikasi sentimen. Hasil evaluasi menunjukkan bahwa IndoBERT yang telah dilakukan fine-tuning mencapai akurasi sebesar 0,91, mengungguli tiga model baseline yaitu TF-IDF dengan SVM, LSTM, serta IndoBERT tanpa fine-tuning. Uji signifikansi statistik menggunakan uji McNemar membuktikan bahwa peningkatan tersebut signifikan secara statistik (p < 0,05). Selain itu, analisis tematik kualitatif mengungkapkan bahwa sentimen negatif didominasi oleh kekhawatiran terhadap harga yang mahal infrastruktur pengisian daya yang minim serta ketidakpercayaan terhadap kebijakan pemerintah sedangkan sentimen positif cenderung berkaitan dengan manfaat lingkungan dan insentif yang adil. Penelitian ini menunjukkan bahwa pendekatan fine-tuning pada IndoBERT secara signifikan meningkatkan akurasi klasifikasi sentimen dan memberikan wawasan berharga mengenai opini publik yang mendukung pengembangan kebijakan dan strategi industri dalam mempromosikan mobilitas ramah lingkungan di Indonesia
DETEKSI INDIKASI GANGGUAN KESEHATAN MENTAL BERBASIS TEKS MENGGUNAKAN NLP DENGAN TEKNIK AUGMENTASI EDA Sherly Dian Tiara; Erna Daniati; Arie Nugroho
The Indonesian Journal of Computer Science Research Vol. 5 No. 2 (2026): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i2.273

Abstract

This study aims to build a classification model for the early screening of mental health disorders from social media text data using the CRISP-DM framework. The primary issue of data imbalance between categories was addressed using the Easy Data Augmentation (EDA) technique. Logistic Regression algorithm and TF-IDF feature extraction were used to classify six categories of mental conditions. Test results showed that the model with EDA experienced a slight decrease in global accuracy to 0.74 (compared to 0.76 without EDA) but successfully increased the Recall for the minority class, Mentalillness, significantly from 0.28 to 0.56. This improvement proves that EDA effectively enriches linguistic variation in limited data. The model has been validated by a psychologist and implemented into a web-based application as an indicative early detection tool, not a clinical medical diagnosis.  
Co-Authors Abdullah Abdullah Achmad Nabila Abas Ahmad Tohari Aidina Ristyawan Akmal Hisyam Pradhana Anindya Purbasari Anita Fatmawati Anita Sari Wardani Ari Kurniawan Bagus Dwi Andika Bifadhlillah Marsheila Islami Cinta Azzaria Claudio Syanu Mareta Dinata Dika Prasetyo Dwi Harini Erna Daniati Ersa Dwi Nur Aini Habibi Aulia Nur Syifa Habibi Jodi Armyanto Kevin Satrio Cahyono Khoirudin Eko Nurcahyo Moh. Azka Wildan Muhammad Fauzan Aditiya Mufid Muhammad Imron Amrulloh Muhammad Junaidi Muhammad Najibulloh Muzaki Nurun Nihayatur Rifqiyah Aulia Putri Ameliya Reka Ainul Khasanah Rina Firliana Rini Indriati Rizal Syihab Saputra Adam Shanti Oktafia Sherla Mutia Sherly Dian Tiara Sucipto Sucipto Teguh Andriyanto Utama, Hastari