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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) SMATIKA Briliant: Jurnal Riset dan Konseptual Journal of Development Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab KOMPUTIKA - Jurnal Sistem Komputer JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Teknologi Komputer dan Sistem Informasi Jurnal ICT : Information Communication & Technology JSAI (Journal Scientific and Applied Informatics) Jurnal Teknologi Informasi dan Multimedia ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Bumigora Information Technology (BITe) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Journal of Computer Networks, Architecture and High Performance Computing Journal of System and Computer Engineering Bima Abdi: Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) GANESHA: Jurnal Pengabdian Masyarakat Journal of Science Nusantara Journal Automation Computer Information System (JACIS) Informatics, Electrical and Electronics Engineering Biner : Jurnal Ilmiah Informatika dan Komputer JAMU : Jurnal Abdi Masyarakat UMUS Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Sains Sistem Informasi
Abd. Charis Fauzan
Program Studi Ilmu Komputer, Fakultas Ilmu Eksakta, Universitas Nahdlatul Ulama Blitar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Comparative Analysis of Machine Learning Models for Identifying Cybercrimes in Social Media Comments Fauzan, Abd. Charis; Arifin, Mochammad; Mafula, Veradella Yuelisa
Jurnal Teknik Elektro dan Informatika Vol 4 No 2 (2024): INFOTRON
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/infotron.v4i2.23069

Abstract

The rapid growth of social media has created opportunities for digital interaction but has also introduced challenges, particularly in addressing cybercrimes such as defamation, threats, and SARA-related content. Cybercrime detection on social media is critical as it helps mitigate the spread of harmful behavior, safeguard users, and support law enforcement in addressing violations like Indonesia's Information and Electronic Transactions Law (UU ITE). This study conducts a comparative analysis of machine learning algorithms—Naive Bayes, Support Vector Machines (SVM), and Random Forests—to identify cybercrimes in social media comments. Using a sentiment-labeled dataset obtained from Kaggle, consisting of Indonesian social media comments from Twitter (X), the comments are categorized into seven specific classes: Neutral Sentiment, Positive Sentiment, Negative Sentiment, Insulting Government, Insulting or Defaming Others, Threatening Others, and SARA-Based Content. The results show that Random Forest achieved the highest overall accuracy (91%) and performed best in detecting moderately represented classes such as Insulting Government. SVM demonstrated robust performance with 88% accuracy, particularly excelling in identifying dominant classes like Negative Sentiment, while Naive Bayes, though computationally efficient, struggled with minority classes, achieving an accuracy of 73%. However, the dataset's imbalance posed challenges for all algorithms, particularly with underrepresented categories. This limitation underscores the need for more diverse and representative datasets to improve model performance and ensure broader applicability of the findings.
Comparison of Lexical and Semantic Approaches for Relevance Measurement in Quranic Verse Translation Retrieval Fauzan, Abd. Charis; Rouf, M. Abd.; Prabowo, Tito; Baqi, Utrodus Said Al
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5194

Abstract

This research explores the effectiveness of lexical and semantic approaches for relevance measurement in Quranic verse translation retrieval, focusing on Indonesian translations. Quranic verses encompass complex linguistic structures and diverse contexts, making precise retrieval challenging. Two retrieval methods were evaluated: lexical similarity, which focuses on exact word matches, and semantic similarity, which captures contextual meaning using word embeddings. The study utilized a dataset of Indonesian Quranic translations, preprocessed to normalize and tokenize text, with experimental queries derived from thematic exegesis on social responsibility. Evaluation was performed using precision, recall, and F1-score on top-5, top-10, and top-15 retrieved results. The lexical approach achieved perfect precision (100%) but exhibited lower recall (46%-58%), as it failed to retrieve relevant verses lacking exact matches. Conversely, the semantic approach demonstrated higher recall (56%-59%) and F1-scores (73%-74%) by identifying verses with contextual relevance, even in the absence of lexical similarity. The results reveal that while the lexical approach ensures precise matches, it overlooks semantic richness. The semantic approach, although computationally intensive, achieves greater contextual understanding. These findings highlight the potential for hybrid retrieval systems combining both approaches to enhance accuracy and relevance in Quranic information retrieval, supporting scholarly research and user engagement with Quranic content.
Analisis Deret Waktu untuk Forecasting Populasi Ternak di Indonesia dengan Model LSTM Prabowo, Tito; Lestariningsih; Fauzan, Abd. Charis; Mafula, Veradella Yuelisa
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.v8i1.7566

Abstract

Livestock population in Indonesia is one of the key indicators supporting national food security, particularly in meeting the demand for animal-based protein. However, the suboptimal utilization of livestock population data for strategic planning remains a challenge in the livestock sector. This study aims to predict livestock population in Indonesia using the Long Short-Term Memory (LSTM) method, a variant of Recurrent Neural Network (RNN) designed for time series data analysis. The livestock population data used in this research was obtained from the Central Statistics Agency (BPS) for the period of 2006 to 2022. The LSTM model was trained using 80% of the data for training and 20% for testing, with evaluation conducted using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the LSTM model can forecast the national livestock population up to 2033 with good accuracy, particularly for livestock such as goats (MAPE 5.47%) and beef cattle (MAPE 5.64%). However, a higher error rate was observed for buffalo (MAPE 16.57%). The predictions indicate a significant growth trend in poultry populations, such as broiler chickens and laying hens. In conclusion, this model can support data-driven decision-making to ensure stable and sustainable animal protein availability, thereby strengthening national food security.
Hill Cipher-Based Visual Cryptography for Copyright Protection of Images Using Flexible Matrix Keys Mafula, Veradella Yuelisa; Fauzan, Abd. Charis; Prabowo, Tito; Ramadhan, Muhammad Rizky
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1634

Abstract

The widespread distribution of digital images on the internet has diminished the copyright protection associated with them. In some cases, copyrighted and economically valuable digital images should not be modified or distributed without permission, as altering the original image can harm its owner. This violation is common, but many internet users are unaware of it. The goal of this research is to protect intellectual property rights of digital images using visual cryptography based on the Hill Cipher algorithm with matrix key flexibility. Hill Cipher is chosen for its ability to encrypt data in blocks, making it more secure than classical cryptographic algorithms that encrypt data individually. Visual cryptography is used to secure digital images through encryption and decryption. Encryption scrambles the image, while decryption restores it. The research method involves collecting digital image datasets, preprocessing, Hill Cipher encryption, and decryption. Key flexibility includes matrix keys of 2x2, 3x3, and 4x4 to enhance security. This research has demonstrated the effectiveness of the Hill Cipher algorithm in protecting digital images through encryption and decryption processes with flexible matrix keys of size 2x2 and 3x3. The results of the experiments, including encryption and decryption using both matrix sizes, have been thoroughly analyzed with respect to various cryptographic metrics: histogram analysis, energy, entropy, and running time.
Analisis dan Perbaikan Proses Bisnis pada Produksi Konveksi Gandes Tailor Menggunakan Teknik Esia Khasanah, Dhea Lutfiatul; Fauzan, Abd. Charis
Jurnal Manajemen Teknologi Dan Sistem Informasi (JMS) Vol 5 No 1 (2025): JMS Vol 5 No 1 Maret 2025
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jms.2025.5.1.2047

Abstract

Konveksi Gandes Tailor merupakan salah satu bidang usaha yang bergerak pada jasa pembuatan pakaian jadi. Konveksi gandes tailor berada di Dsn. Besole, RT04/RW04, Ds. Darungan, Kec. Kademangan, Kab. Blitar. Pada penelitian ini bertujuan untuk menganalisis dan memperbaiki proses bisnis pada konveksi gandes tailor dengan menggunakan metode ESIA (Eliminate, Simplify, Integrate, Automate). Teknik ESIA digunakan untuk mengidentifikasi dan menghilangkan aktivitas yang tidak menambah nilai, menyederhanakan proses yang kompleks, mengintegrasikan proses yang berulang, dan mengotomatisasi aktivitas yang memakan waktu. Hasil dari analisis menunjukkan bahwa terdapat beberapa aktivitas yang dapat dihilangkan, disederhanakan, digabungkan dan diotomatisasi, setelah mengalami perbaikan proses bisnis konveksi gandes tailor saat ini menghasilkan 26112 menit yang menunjukkan bahwa konveksi gandes tailor masih memiliki beberapa proses untuk diperbaiki. Setelah di lakukan perbaikan menggunakan Teknik esia, konveksi gandes tailor menghasilkan 12909 menit sehingga meningkatkan efisiensi sebesar 76,02% dari perhitungan improvement.
Leveraging BiLSTM for Deep Learning-Based Mental Health Chatbots Agustina, Nur Afnis; Fauzan, Abd. Charis; Harliana, Harliana
Jurnal Teknik Elektro dan Informatika Vol 5 No 1 (2025): INFOTRON
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/infotron.v5i1.23242

Abstract

The high prevalence of mental health issues and limited access to professional information and support have driven the search for innovative solutions. One promising approach is the development of chatbot systems that provide quick and accessible mental health information. This study evaluates the performance of the Bidirectional Long Short-Term Memory (BiLSTM) algorithm in identifying and classifying user inputs within a mental health chatbot system. BiLSTM is chosen for its ability to process sequential data in both directions, allowing it to capture context more effectively than unidirectional models and better understand user intent. Deep learning methods like BiLSTM have also demonstrated higher accuracy compared to traditional machine learning models. This study focuses solely on BiLSTM to evaluate its performance in this context. The mental health dataset used in this study was sourced from previous research published on the GitHub platform and contains 100 classes of mental health-related questions and statements. This dataset was used to train the BiLSTM model to recognize user intent and generate relevant responses. The model achieved 98% accuracy on the training data. For evaluation on the test set, a confusion matrix was used, yielding an accuracy of 82%. The chatbot is implemented as a web-based application using a Python framework and is designed to provide users with insights and knowledge through text-based interactions. These results highlight the potential of the BiLSTM-based chatbot system to deliver effective and efficient mental health information services
KLASIFIKASI CITRA CACAR MONYET MENGGUNAKAN GRAY LEVEL CO-OCCURRENCE MATRIX DAN ALGORITMA LINEAR DISCRIMINANT ANALYSIS Fauzan, Abd. Charis; Maskuri, Muhammad Naja
Biner : Jurnal Ilmiah Informatika dan Komputer Vol 4 No 2 (2025): Juli
Publisher : Program Studi Teknik Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Sains Al-Qur'an

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/biner.v4i2.8973

Abstract

Monkeypox atau cacar monyet merupakan penyakit menular yang disebabkan oleh virus orthopoxvirus yang berasal dari hewan primata dan hewan pengerat. Berdasarkan data WHO sejak Januari 2022 sampai Juni 2023 terdapat 88.060 kasus terkonfirmasi cacar monyet dan 147 kasus kematian akibat cacar monyet yang tersebar di 112 negara di dunia. Penyebaran kasus cacar monyet yang terus meluas diberbagai negara, membuat cacar monyet menjadi salah satu penyakit yang banyak diperbincangkan. Pada penelitian ini dilakukan proses identifikasi citra lesi penyakit cacar monyet dan citra non-cacar monyet (campak dan cacar ayam) dengan melalui tahap preprocessing, tahap ekstraksi ciri GLCM dengan 7 fitur (contras, correlation, energi, homogenitas, entropi, mean dan variance), dan tahap pelatihan model pembelajaran mesin klasifikasi menggunakan algoritma LDA. Melalui proses evaluasi 10-fold cross validation didapakan hasil evaluasi model pembelajaran klasifikasi yang dibangun menunjukkan nilai performa rata-rata akurasi sebesar 80,55%, presisi 78%, recall 80%, dan spesificity 79%. Hasil ini menunjukkan bahwa model klasifikasi yang dikembangkan memiliki performa yang baik (good classification) dalam membedakan citra cacar monyet dan non-cacar monyet.
Coral Reef Image Classification Using Multilayer Perceptron Fauzan, Abd. Charis; Sari, Hetty Elvina
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.32134

Abstract

Coral reefs are one of the marine organisms that play many crucial roles for other organisms within them. Coral reefs are often referred to as tropical rainforests because they serve as shelters for small fish and produce food for other marine organisms. Over time, various threats have emerged that disrupt the stability of the marine ecosystem, one of which is coral reef degradation, such as bleaching or physical damage caused by multiple factors. These factors include climate change, chemicals resulting from fishing with explosives, and pollution. As a result, coral reefs become damaged and can no longer serve as a refuge for small species.  Therefore, this study aims to mitigate the impact of coral reef damage by developing a coral reef classification model using one of the deep learning algorithms and artificial neural networks, namely the Multilayer Perceptron (MLP), which employs multiple hidden layers in its modeling process. The classification results using this algorithm achieved an accuracy of 73%, indicating that the model performs well in classifying coral reefs in image form. Thus, it is hoped that deep learning innovations for coral reef classification can contribute significantly to coral reef conservation and marine resource management.
Co-Authors A. Zainul Muttaqin Sph Achmad Nuuril Faizin Adhitya Prayoga Permana Afivatu Pratama Agustin Afrida Danar Pratama Agus Yulianto Agustin Ely Rahayu Agustin Ely Rahayu Agustina, Nur Afnis Ahmad Agung Saputra Ahmad Fayyadh Qaimul Haq Ahmad Gufron Ahmad Makhi Ahmad Makhi Ahmad Yunus Ainafatul Nur Muslikah Alvi Durunnafis Annisa Heparyanti Safitri Ardisca Evanandy Asfilia N. Anggraini Asna Andi Auladi Ayu Eviana Baqi, Utrodus Said Al Bharin Rizqi Waridhon Binti Aulatul Mufida Bunga Cahyaning Untari Caska - Chandra Gunawan Choirunnufatul Chusna Citra Mirna Wati Defy Lukbatul Qolbiah Deny Restyo Nugroho Dewana Firman Abdul Mu'izz Dewi Lestari Dimas Fahmi Rizaldi Dwi Danu Handoyono Dwi Zulva Uliunuha Dyah Ayu Wiranti Edoardo Jofan Rifano Edy Prabowo Egy Nadya Etvin Trio Sagita Fadhila Nur Hanifah Faizin Choirul Umam Fatmawati, Fariza Uma FATRA NONGGALA PUTRA Fiqih Ainul Qhabib Gunawan Gunawan Hardiana Riski Riswanto Harliana Harliana Harliana Harliana Harliana Harliana Hinayu Diniatul Fahma Huda Maariful Muhamat Ibrohim Aqimuddin Ida Ayu Putu Sri Widnyani Ike Wahyu Septiani Ilham Kurniawan Imam Machfud Indana Nuril Hidayah Indera Cahyo Wibowo Iqbal Fikri Al hadi Iza Arfiana Fauziah Jayanti Galuh Condrokirono Junaedi Abdillah Kafi, Elfiya Khanatul Kasanah, Siti Uswatun Khamaida Safinah Kharisma Sabbihatul Mustaghfaroh Khasanah, Dhea Lutfiatul Khoiril Hikmah Khoiril Hikmah Khoiril Hikmah Kurnia Siwi Kinasih Kurnia Z. Matondang Kurniyatul Ainiyah Lestariningsih Lestariningsih Lestariningsih Linda Salma Angreani M Maulana Ikhsan M. Subhan Ansori Machmud Naufal Mafula, Veradella Yuelisa Marshella Dwi Putri Yustiana Maskuri, Muhammad Naja Mochamad Buqori Muslim Mochammad Arifin Mochammad Rizqy Pratama Moh Afif Rofiqi Moh Bagus Sholikul Adib Muhamat Maariful Huda Muhamat Maariful Huda Muhammad Ammarullah Ridho Muhammad Asfa Ilhami Muhammad Holili Muhammad Nasyithul Ibad Muhammad Rizky Ramadhan Muhammad Yusuf Ashari Muhammat Maariful Huda Munziah Ahmad Nadila Oktavia Ningtias Naela Nur Choiriyah Nahdiyah, Umi Nanang Zamroji Nanik Yustia Neni Febiani Nurul Aziz Tri Wahyuni Panky Yoga Pratama Prabowo, Tito Puan Maharani Kurniawan Putri Handayani Raden Mohamad Herdian Bhakti Rahmat Yanu Sutrisno Redhitya Wempi Ansori Riska Fitri Nur Alifah Rouf, M. Abd. Safrinadi Ilham Salma Fatia Salnan Sabdo Wibowo Salsabella Elizzah Sari, Hetty Elvina Seta Murdha Pamungkas Setyawan, Dimas Ari Shinta Rizki Firdina Sugiono Shofiyatun Najah Shofiyatun Najah Subana Subana Sugeng Wahyudi Suraji Suraji Syarif Alqoroni Tamaulina Br Sembiring Tito Prabowo Toto Ricky Fernando Tricahyo, Vion Age Utrodus Said Al Baqi Veradella Yuelisa Mafula Veradella Yuelisa Mafula Wasimin Wasimin Winda Puji Larasati Yayang Galuh Nur Khamidatullailiyah Yeni Ratih Pratiwi Zainun Nasikin