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All Journal Jurnal Pendidikan Teknologi dan Kejuruan MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminas Competitive Advantage Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Journal of Development Research JOIV : International Journal on Informatics Visualization Applied Information System and Management IJISTECH (International Journal Of Information System & Technology) Systemic: Information System and Informatics Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Mnemonic International Journal of Advances in Data and Information Systems IJISTECH Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Janitra Informatika dan Sistem Informasi Bulletin of Social Informatics Theory and Application Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Yunifa Miftachul Arif
Department Of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim Malang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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SISTEM REKOMENDASI MATERI PEMROGRAMAN WEB PADA MEDIA PEMBELAJARAN BERBASIS WEB MENGGUNAKAN MULTI-CRITERIA RECOMMENDER SYSTEM Wahyuliningtyas, Lia; Miftachul Arif, Yunifa; Kusumawati, Ririen
Jurnal Mnemonic Vol 7 No 1 (2024): Mnemonic Vol. 7 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v7i1.8128

Abstract

Dalam kurikulum merdeka, pembelajaran yang dilakukan fokus terhadap pengembangan karakter, kompetensi siswa dan mengasah minat bakat. Sehingga jumlah materi pembelajaran yang diberikan kepada siswa tidak harus tuntas atau lebih sedikit. Selain itu pada kurikulum merdeka tidak lagi membebani siswa dengan ketercapaian skor minimal karena penilaian tidak lagi menggunakan nilai Kriteria Ketuntasan Minimal (KKM). Hal tersebut menyebabkan guru kesulitan menentukan apakah materi yang telah dijelaskan sudah dapat dipahami karena nilai tidak menjadi patokan dalam keberhasilan seorang siswa. Padahal apabila guru tidak mengetahui pemahaman seoarang siswa, guru akan kesulitan untuk lanjut pada materi selanjutnya. Implementasi Multi-Criteria Recommender System (MCRS) dapat memberikan kemudahan guru untuk dapat memprediksi apakah siswa dapat lanjut ke materi selanjutnya dan merekomendasikan modul mana yang cocok untuk siswa tersebut. Sistem rekomendasi yang akan dibangun berupa media pembelajaran berbasis web agar siswa dapat lebih tertarik dan dapat membantu guru dalam meningkatkan hasil belajar. Metode yang digunakan adalah collaborative filtering dengan membandingkan antara adjusted cosine similarity, cosine based similarity dan spearman rank order correlation. Berdasarkan implementasi MCRS menggunakan metode collaborative filtering menunjukkan bahwa hasil sistem rekomendasi tersebut memberikan dampak yang baik untuk proses belajar mengajar. Berdasarkan 3 algoritma yang diimplementasikan bahwa hasil prediksi yang paling baik adalah cosine based similarity karena nilai MAE yang didapatkan paling rendah yaitu sebesar 1,19 dan nilai akurasi sebesar 76%.
Extended reality for education: Mapping current trends, challenges, and applications Samala, Agariadne Dwinggo; Bojic, Ljubisa; Rawas, Soha; Howard, Natalie-Jane; Arif, Yunifa Miftachul; Tsoy, Dana; Coelho, Diogo Pereira
Jurnal Pendidikan Teknologi Kejuruan Vol 7 No 3 (2024): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v7i3.37623

Abstract

The advancements in 5G technology and Artificial Intelligence (AI) have accelerated the integration of immersive technologies such as Extended Reality (XR) into educational practices. There is a notable scarcity of studies focusing specifically on the applications and impact of XR in academic settings. Most existing research has concentrated on AR and VR, leaving a gap in understanding the full potential of XR. Addressing these gaps and challenges is crucial for harnessing the full potential of XR in education. This study aims to map and analyze the applications, trends, and educational challenges of XR technology. This study conducts a bibliometric analysis covering XR's application in education from 2018 to 2023, analyzing 32 articles from Scopus sources. Key findings highlight XR's annual growth in research publications, with significant contributions from the United States, China, and Canada. XR enriches education by facilitating immersive simulations, real time interaction with virtual objects, and spatial manipulation in three dimensions. It fosters presence and embodiment in virtual environments, supports practical training through realistic simulations, enhances multi-sensory engagement, promotes collaborative learning environments, and improves accessibility for diverse learners. The main challenges of XR technology include high costs, technical hurdles, regulatory issues, infrastructure limitations, and the need for digital literacy and skills. Addressing these challenges, collaborative efforts among educators, researchers, and industry stakeholders are required. Such collaboration is crucial for harnessing the full potential of XR technology to revolutionize education and prepare learners for a dynamic future.
Applying convolutional neural network and Nadam optimization in flower classification Aini, Qurrotul; Zulfiandri, Zulfiandri; Firmansyah, Rezky; Arif, Yunifa Miftachul
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6203

Abstract

Flowers have a variety of shapes, colors and structures, the images of which need to be classified using guided learning techniques. Several studies classify flowers using machine learning, but their accuracy performance is not good. The thing is, the flowers come in a variety of colors that can sometimes look similar to the background. Therefore, this study aims to classify flowers using a convolutional neural network (CNN) and measure its performance. The method used is mixed methods by collecting existing data from previous studies and connecting it with the realities in the field. The Kozłowski and Steinbrener models were used, while the image data was obtained from the Oxford17 and Oxford102 dataset with 17 and 102 flower types, respectively. The results show 60% and 84% accuracy of CNN using the scratch and transfer learning approach for the Oxford17 dataset. The Oxford102 dataset shows 42% and 64%, respectively, with CNN from baseline and transfer learning.
ANALISIS SENTIMEN ARTIKEL BERITA PEMILU BERBASIS METODE KLASIFIKASI Fathir; Hariyadi, M. Amin; Miftachul A, Yunifa
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 2 (2023): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i2.220

Abstract

The distribution of information in the form of online news is so massive in the wider community, that it is difficult to distinguish between haox news and positive news. So that a classification is needed regarding public sentiment about the implementation of elections using mainstream media news article data using 1064 dataset test data. The methods used in this study are the naive Bayes algorithm, the random forest algorithm, and the support vector machine algorithm. The test model uses smote where the performance results are carried out by the algorithm used using smote and not using smote, where random forest produces an accuracy of 91.88%, while without using a smote support vector machine it produces an accuracy of 92.05%.
CSS for CVR: A Reciprocal Velocity Obstacle-Based Crowd Simulation System for Non-Playable Character Movement of Campus Virtual Reality Arif, Yunifa Miftachul; Janitra, Geovanni Azam; Imamudin, M.; Safitri A Basid, Puspa Miladin Nuraida; Setiawan, Dedy Kurnia
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1997

Abstract

Along with the development of multimedia technology, an overview of the campus environment for prospective new visitors can be visualized through a 3D virtual environment based on virtual reality. A crowd simulation system is needed to provide an overview of the crowds in campus virtual reality (CVR). The simulation helps make it easier for individuals to predict crowds in certain areas virtually. In this study, we propose using the Reciprocal Velocity Obstacle (RVO) method to support the simulation of Non-Playable Character (NPC) crowds in a visualized virtual environment. RVO implements multi-agent navigation by estimating the possibility of moving without communication between agents and being able to perform collision avoidance. The use of RVO in this study aims to contribute to the collision detection development process for each NPC. The application of RVO is carried out in the development of virtual reality by using Unity3D and Blender asset support tools. The results of this study indicate that the RVO method can be applied in multi-agent navigation. These results were confirmed by the success of the NPC as a simulation agent in selecting routes and independently navigating to avoid collisions between agents without the need for communication. In every simulation, collisions will occur within a set of agents due to high density, which causes complex computations. The development of CSS can help every CVR user experience a virtual environment. In addition, each user can experience a more natural experience with the presence of 3D objects and virtual reality with RVO-based CSS. Furthermore, this research material is expected to be developed from various perspectives and themes related to crowd simulation for games and other simulation media.
Sistem Rekomendasi Destinasi Wisata di Kota Batu Menggunakan Neural Collaborative Filtering Aristantia, Yuliana; Arif, Yunifa Miftachul; Abidin, Zainal
ILKOMNIKA Vol 6 No 3 (2024): Volume 6, Nomor 3, Desember 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v6i3.688

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi destinasi wisata di Kota Batu, Jawa Timur, menggunakan metode Neural Collaborative Filtering (NCF). Sistem ini dirancang untuk membantu wisatawan memilih destinasi yang sesuai dengan preferensi mereka, mengatasi tantangan keberagaman kebutuhan wisatawan dalam industri pariwisata yang terus berkembang. Dataset yang digunakan mencakup data pengunjung, preferensi wisata, dan informasi 14 lokasi wisata populer di Kota Batu. Proses implementasi melibatkan persiapan dataset, pembentukan matriks embedding, dan pelatihan model menggunakan TensorFlow. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall berdasarkan Confusion Matrix. Hasil penelitian menunjukkan bahwa sistem rekomendasi berbasis NCF memiliki performa akurasi yang baik, dengan akurasi tertinggi sebesar 81% pada skenario pembagian data training 80% dan data testing 20%. Skenario lainnya, seperti 85:15, menghasilkan akurasi 80%, yang mengindikasikan kemampuan generalisasi model yang memadai. Namun, akurasi menurun pada skenario dengan proporsi data training yang lebih kecil, seperti 60:40 (67%) dan 70:30 (74%), menegaskan pentingnya dataset yang representatif untuk meningkatkan prediksi. Sistem ini efektif dalam memberikan rekomendasi yang personal dan akurat, sekaligus mendukung promosi pariwisata di Kota Batu. Penelitian ini diharapkan dapat mendorong peningkatan kunjungan wisatawan ke Kota Batu dan berkontribusi pada pertumbuhan industri pariwisata Indonesia.
Struggling Models: An Analysis of Logistic Regression and Random Forest in Predicting Repeat Buyers with Imbalanced Performance Metrics Mauludiah, Siska Farizah; Arif, Yunifa Miftachul; Faisal, Muhammad; Putra, Dony Darmawan
Applied Information System and Management (AISM) Vol 7, No 2 (2024): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v7i2.39326

Abstract

Predicting repeat buyers is essential for businesses seeking to improve customer retention and maximize profitability. This study examines the effectiveness of logistic regression and random forest algorithms in forecasting repeat buyers, utilizing an e-commerce dataset from Kaggle. Despite the theoretical strengths of these models, our results indicate significant performance challenges. Both models were evaluated on key metrics: accuracy, precision, recall, F1 score, and ROC-AUC. The findings revealed that the models logistic regression and random forest performed poorly, with accuracy hovering around 50%, precision and recall demonstrating imbalanced performance, and ROC-AUC scores barely exceeding random guessing levels. Such metrics highlight the limited discriminative power of these models in identifying repeat buyers. The analysis suggests that issues such as data quality, feature relevance, and class imbalance contribute to these shortcomings. Specifically, the models struggled to effectively learn from the data, leading to suboptimal predictions. These results underscore the need for enhanced feature engineering, better handling of class imbalance, and possibly exploring more advanced algorithms. This study provides a critical assessment of the limitations inherent in using Logistic Regression and Random Forest for predicting repeat buyers, hence implements feature engineering, SMOTE and hyperparameter tuning using RandomSearchCV to get better result.
Employing Multi-Layer Perceptron Models for Heart Failure Disease Prediction Hamid, Abdulhalim Hamid Salih; Arif, Yunifa Miftachul; Hariyadi, M. Amin
Journal of Development Research Vol. 9 No. 1 (2025): Volume 9, Number 1, May 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v9i1.430

Abstract

This study aims to develop a predictive model for heart failure using a multilayer perceptron (MLP) as part of the application of deep learning techniques in medical data analysis. Given the increasing prevalence of heart failure and its significant impact on patients' quality of life and healthcare costs, early detection is of paramount importance. The dataset, obtained from Kaggle, consists of 918 medical records containing 12 key health variables, including age, blood pressure, cholesterol level, and fasting blood sugar. The model underwent extensive training and testing, and its performance was evaluated using statistical measures such as precision, recall, accuracy, and AUC-ROC curve. The results showed that the proposed model achieved a prediction accuracy of 91.1%, with a sensitivity of 90.3% and a specificity of 92%, indicating its effectiveness in predicting heart failure compared to traditional models. Further analysis identified ST-segment depression, resting blood pressure, and cholesterol level as the most influential factors in determining the risk of heart failure. Based on these results, the MLP model can be considered an effective tool to assist physicians in the early diagnosis of heart failure. Optimization techniques such as particle swarm optimization (PSO) can be used to improve prediction accuracy. Furthermore, combining the model with advanced analytical methods may enhance its predictive performance. This study highlights the importance of using artificial neural networks in the medical field, emphasizing their role in improving early diagnosis systems, reducing heart failure complications, and improving the overall quality of healthcare services.
Enhancing Repeat Buyer Classification with Multi Feature Engineering in Logistic Regression Mauludiah, Siska Farizah; Crysdian, Cahyo; Arif, Yunifa Miftachul
Applied Information System and Management (AISM) Vol 8, No 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45025

Abstract

This study presents a novel approach to improving repeat buyer classification on e-commerce platforms by integrating Kullback-Leibler (KL) divergence with logistic regression and focused feature engineering techniques. Repeat buyers are a critical segment for driving long-term revenue and customer retention, yet identifying them accurately poses challenges due to class imbalance and the complexity of consumer behavior. This research uses KL divergence in a new way to help choose important features and evaluate the model, making it easier to understand and more effective at classifying repeat buyers, unlike traditional methods. Using a real-world dataset from Indonesian e-commerce with 1,000 records, divided into 80% for training and 20% for testing, the study uses logistic regression along with techniques like SMOTE for oversampling, class weighting, and regularization to fix issues with data imbalance and overfitting. Model performance is assessed using accuracy, precision, recall, F1-score, and KL divergence. Experimental results indicate that the KL-enhanced logistic regression model significantly outperforms the baseline, especially in balancing precision and recall for the minority class of repeat buyers. The unique contribution of this work lies in its synergistic use of KL divergence in both the feature engineering and evaluation phases, offering a robust, interpreted, and data-efficient solution. For e-commerce businesses, the findings translate into improved targeting of high-value customers, better personalization of marketing efforts, and more strategic allocation of resources. This research offers practical tips for enhancing predictive customer analytics and supports data-driven decision-making in digital commerce environments.
Sistem Pengawasan CCTV Pada ATM Secara Real-Time Berbasis Internet of Things Setiyawan, Niko Heri; Hariyadi, Mokhamad Amin; Arif, Yunifa Miftachul
Jurnal Janitra Informatika dan Sistem Informasi Vol. 5 No. 1 (2025): April - Jurnal Janitra Informatika dan Sistem Informasi
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/k0e60m33

Abstract

Efisiensi laporan kriminalitas terhadap CCTV merujuk pada kemampuan sistem pengawasan menggunakan kamera CCTV dalam mempercepat, mempermudah, dan meningkatkan akurasi proses pelaporan tindakan kriminal. Dengan kata lain, efisiensi ini mencakup bagaimana penggunaan CCTV, terutama yang berbasis Internet of Things (IoT), dapat mengurangi waktu dan tenaga dalam mendeteksi, merekam, serta menyampaikan informasi tentang kejadian kriminal kepada pihak yang berwenang (Lutviansyah, 2025). Penelitian ini termasuk dalam kategori penelitian eksperimen kuantitatif yang bertujuan untuk mengukur efisiensi pelaporan tindakan kriminal melalui penerapan sistem CCTV berbasis IoT pada mesin ATM. Penelitian ini juga menguji pengaruh protokol MQTT terhadap kecepatan transmisi data dan efisiensi waktu pelaporan. sistem pengawasan CCTV berbasis Internet of Things (IoT) dengan protokol MQTT memberikan efisiensi yang signifikan dalam pengawasan keamanan ATM secara real-time. Sistem ini mampu mempercepat waktu deteksi kejadian kriminalitas, dari sebelumnya rata-rata 120 menit menjadi 15 menit. Mempercepat waktu pelaporan ke pusat keamanan, dari 90 menit menjadi 10 menit, melalui notifikasi otomatis tanpa intervensi manual. Menghemat konsumsi bandwidth, dari 1024 Kbps pada sistem konvensional menjadi hanya 256 Kbps dengan pendekatan event-based MQTT. Mempercepat waktu respon keamanan, dari 30 menit menjadi 5 menit, karena petugas menerima laporan secara langsung dan instan. Meningkatkan keberhasilan pemantauan real-time, dari 60% pada sistem lama menjadi 100% dalam mendeteksi kejadian saat berlangsung.
Co-Authors AA Sudharmawan, AA Achmad Sabar, Achmad Ady Wicaksono Agariadne Dwinggo Samala Alfachruddin, M. Nabil Fahd Aristantia, Yuliana Bojic, Ljubisa Cahyo Crysdian Coelho, Diogo Pereira Dedy Kurnia Setiawan Duvan Deswantara Putra Dyah Wardani Fachrul Kurniawan Fathir Fathir Fathir Fathurrahman Firmansyah, Rezky Fresy Nugroho Hamid, Abdulhalim Hamid Salih Hani Nurhayati Howard, Natalie-Jane Ihsan, Afif Nuril Ikhlayel, Mohammed Imamudin, M. Janitra, Geovanni Azam Juniardi Nur Fadila Linda Salma Angreani Mauludiah, Siska Farizah Mauridhi H Purnomo Mochamad Hariadi Mochammad Wahyu Firmansyah Mokhamad Amin Hariyadi Muhammad Faisal Muhammad Faisal Muhammad Sahi Nauman Khan Norizan Mat Diah Putra, Dony Darmawan Rawas, Soha Ririen Kusumawati Rizqi Aulia Nadhifah Rony, Zahara Tussoleha Roro Inda Melani Safitri A Basid, Puspa Miladin Nuraida Setiyawan, Niko Heri Sulika Sulika Supeno Mardi S. N, Supeno Mardi Supeno Mardi Susiki Nugroho, Supeno Mardi Supriyono Tsoy, Dana Wahyuliningtyas, Lia Zainal Abidin Zulfiandri Zulfiandri