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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo Building of Informatics, Technology and Science SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Innovative: Journal Of Social Science Research Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Manajemen Sistem Informasi The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik
Afrizal Nehemia Toscany
Universitas Dinamika Bangsa
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Public Health Other

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Perancangan Sistem Penerbitan dan Verifikasi E-Ijazah dan E-Transkrip Menggunakan Teknologi Blockchain pada Universitas Dinamika Bangsa Toscany, Afrizal; Bustasmi, M.Irwan; Saputra, Chindra
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3082

Abstract

With the development of increasingly sophisticated technology, the act of counterfeiting becomes an easy thing to do. An image editor application intended to heed a work of art, has now turned its function into a tool for document forgery.  Forgery of a diploma is a form of deviant behavior that violates the rule of law and makes the perpetrator criminal. Economic conditions, low education and the need to get a job are reasons for perpetrators to commit forgery. In addition, political motives are also often a reason to get political office.  One of the technologies to anticipate this problem is to apply blockcerts to electronic diplomas. Blockcert is built using blockchain technology that provides transparency and accountability in the storage of certificates and diplomas. This research resulted in a system of publishing and validating e-diplomas and e-transcripts at the University of Dinamika Bangsa which are stored on a private blockchain. The system has been tested and all modules are running properly according to the expected output.
Perancangan Aplikasi Mobile Learning Berbasis Android Pada Bimbel Infinits Kuala Tungkal Muhammad Ramadhan Saputra; Irawan, Beni; Afrizal Nehemia Toscany
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 4 No 1 (2024): JAKAKOM Vol 4 No 1 APRIL 2024
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2024.4.1.1675

Abstract

Mobile learning adalah salah satu bentuk pembelajaran yang menggunakan teknologi informasi bergerak (IT) untuk belajar atau mengakses materi pelajaran secara fleksibel, kapan saja, dan di mana saja. Mobile learning memiliki sejumlah keunggulan, termasuk efisiensi waktu dalam proses belajar-mengajar serta mendorong siswa untuk menjadi lebih mandiri dalam mendapatkan pengetahuan. Infinits adalah sebuah lembaga bimbingan belajar (Bimbel) yang terletak di Kuala Tungkal, Kabupaten Tanjung Jabung Barat, Provinsi Jambi. Saat ini, Infinits masih menggunakan metode pembelajaran konvensional dan belum memanfaatkan mobile learning (m-learning) dalam proses pembelajaran. Hal ini mengakibatkan beberapa kendala, seperti kesulitan siswa dalam mengulang materi dan pemahaman pelajaran yang terbatas. Selain itu, absensi guru atau siswa juga dapat menghambat kelancaran proses belajar-mengajar. Oleh karena itu, penulis merancang sebuah aplikasi mobile learning berbasis Android untuk mengatasi masalah pembelajaran yang dihadapi oleh siswa di Infinits. Pendekatan yang digunakan adalah metode waterfall, dan aplikasi ini dirancang untuk menyajikan materi pembelajaran interaktif dan kuis. Dengan memanfaatkan m-learning, diharapkan aplikasi ini dapat membantu siswa dalam proses belajar di Bimbel Infinits Kuala Tungkal.
Enhancing Fake News Detection on Imbalanced Data Using Resampling Techniques and Classical Machine Learning Models Abidin, Dodo Zaenal; Siswanto, Agus; Saputra, Chindra; Betantiyo , Betantiyo; Nehemia Toscany, Afrizal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5177

Abstract

Class imbalance remains a critical challenge in fake news detection, particularly in domains such as entertainment media where class distributions are highly skewed. This study evaluates seven resampling techniques—Random Oversampling, SMOTE, ADASYN, Random Undersampling, Tomek Links, NearMiss, and No Resampling—applied to three classical machine learning models: Logistic Regression, Support Vector Machine (SVM), and Random Forest. Using the imbalanced GossipCop dataset comprising 24,102 news headlines, the proposed pipeline integrates TF-IDF vectorization, stratified 3-fold cross-validation, and five evaluation metrics: F1-score, precision, recall, ROC AUC, and PR AUC. Experimental results show that oversampling methods, particularly SMOTE and Random Oversampling, substantially improve minority class (fake news) detection. Among all model–resampling combinations, SVM with SMOTE achieved the highest performance (F1-score = 0.67, PR AUC = 0.74), demonstrating its robustness in handling imbalanced short-text classification. Conversely, undersampling methods frequently reduced recall, especially with ensemble models like Random Forest. This approach enhances model robustness in fake news detection on skewed datasets and contributes a reproducible, domain-specific framework for developing more reliable misinformation classifiers.
Design of the Attendance System using RFID and Similarity Metric Learning at Universitas Dinamika Bangsa Toscany, Afrizal; Rahim, Abdul; Bustami, Irwan; Sadikin, Ali
The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i1.3033

Abstract

Admission of new students at Universitas Dinamika Bangsa has a significant increase, so that the number of active students on campus becomes more. This has an impact on the recapitulation process of lecture absence by study staff to be longer than before, due to the calculation process that is done manually. This research will create a system designed to increase efficiency in lecture attendance activities by presenting an attendance system equipped with an RFID (Radio Frequency Identification) Reader and camera. In addition to the process of digitization of attendance data will be added similiarity metric learning method for validation of student absences that are done periodically. The method to be used in system development is prototyping. This research resulted in a system design with UML modeling and absentee device design.
Deteksi Serangan ARP Spoofing MITM pada Jaringan IoT menggunakan Metode Random Forest dan Robust PCA Agung Islamy Aryanto; Yovi Pratama; Afrizal Nehemia Toscany
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.141

Abstract

ARP spoofing attacks are a serious threat to network security, particularly in vulnerable Internet of Things (IoT) environments. This final project aims to detect ARP spoofing attacks on IoT net-works using a combination of Random Forest (RF) and Robust PCA methods. RF is chosen for its classification capabilities and handling of non-linear data, while Robust PCA is used for di-mensionality reduction and handling outliers in the data. The dataset used is "MITMArpSpoof-ing.pcap.csv," which contains network traffic data. The data is processed by performing prepro-cessing, feature scaling, and converting labels to binary (0 for benign, 1 for ARP spoofing). Subsequently, Robust PCA is applied to reduce data dimensions, and then the data is trained using the RF model. The test results show that the RF model with Robust PCA achieves an accu-racy of 96.02% in detecting ARP spoofing attacks. This method has proven effective in identify-ing and classifying ARP spoofing attacks on IoT networks.
Optimized RoBERTa–DeBERTa Ensemble for Multi-Class Sentiment Analysis on Highly Imbalanced Data Sika, Xaverius; Kisbianty, Desi; Istoningtyas, Marrylinteri; Abidin, Dodo Zaenal; Toscany, Afrizal Nehemia
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5350

Abstract

Multi-class sentiment analysis on highly imbalanced datasets poses substantial challenges for achieving accurate and equitable classification, particularly when neutral sentiments are considerably underrepresented. This study evaluates four fine-tuned transformer models—Bidirectional Encoder Representations from Transformers (BERT), DistilBERT, RoBERTa, and DeBERTa—using a real-world Amazon review dataset comprising over 20,000 user-generated texts. Sentiment labels were derived from star ratings through a standardized mapping scheme. Experimental results show that while BERT achieved the highest overall accuracy (93%), its performance on the minority Neutral class remained limited (F1-score: 0.36). DeBERTa improved Neutral recall to 0.59 but with a slightly lower overall accuracy of 91%. To address this imbalance, two ensemble strategies were explored: a fixed-weight soft voting scheme and an optimized-weight ensemble combining RoBERTa and DeBERTa. The optimized RoBERTa–DeBERTa ensemble yielded the most balanced performance, achieving a Neutral-class F1-score of 0.57 while maintaining 91% overall accuracy. ROC and PR curve analyses further indicate superior sensitivity–precision balance for this optimized ensemble. The findings indicate that adaptive ensemble weighting can substantially enhance minority-class detection under severe imbalance. This study provides a clear methodological contribution by demonstrating the effectiveness of targeted ensemble optimization and offers practical guidance for developing more balanced and reliable sentiment classification systems.
Co-Authors Abd. Rasyid Syamsuri Abdul Rahim Abdul Rahim abdurrohman maulana Abidin, Dodo Zaenal Agung Islamy Aryanto Agus Nugroho Agus Siswanto Ali Sadikin Ali Sadikin Almustaqim, Andry Amir, Diana Arahmad Taupiq Arjuna Panji Arjuna Panji Prakarsa Beni Irawan Betantiyo , Betantiyo Bustasmi, M.Irwan Cahyana Putra Pratama Chindra Saputra Desi Kisbianty, Desi Dodo Zainal Abidin Elita Rahmi Husaein, Ahmad Irawan Irawan Irawan, Beni Istoningtyas , Marrylinteri Istoningtyas, Marrylinteri Janu Hadi Susilo Jasmir Jumersyah Pratama, Raka KARMAN, ZULFI Kisbianty , Desi Kurniabudi Lasmedi Afuan M Irwan Bustami M. Irwan Bustasmi M.Irwan M.Irwan Bustasmi Mhd Ridho Saputra Muhammad Ramadhan Saputra MUHAMMAD SURYA Nugroho, M. Restu Nurhadi Nurmala Viani Dwi Rahayu Pareza Alam Jusia Renaldi Yulvianda Rifqi Pratama Putra ROBY SETIAWAN Rts. Fanny Inayah Rusdianto Roestam Setiawan Assegaf SIKA, XAVERIUS Silvi Febrianti Sutoyo, Mochammad Arief Hermawan taupiq, Arahmad Wahyu Nugraha Xaverius Sika Xaverius Sika YOSE, IMELDA Yovi Pratama