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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Conference on Innovation and Application of Science and Technology (CIASTECH) Petir Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Telematika Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Computer Science and Information Technologies TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Scientific Student Journal for Information, Technology and Science Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Buana Pengabdian Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Scientific Journal of Informatics
Siregar, Amril Mutoi
Teknik Informatika, Universitas Buana Perjuangan Karawang
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Comparison Model Optimal Machine Learning Model With Feature Extraction for Heart Attack Disease Classification Salsa Desmalia; Amril Mutoi Siregar; Kiki Ahmad Baihaqi; Tatang Rohana
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.4561

Abstract

Purpose: The purpose of this study is to classify the number of people affected by heart disease and those not affected by heart disease based on various categories of heart attack causes. This study aims to urge people to take better care of their health and to serve as a reference for doctors to educate patients about the dangers of heart attacks. Methods: The model will be constructed via a machine learning methodology. The algorithms utilized in its development encompass the Support Vector Machine (SVM) algorithm, the K-Nearest Neighbor (k-NN) algorithm, and the Random Forest (RF) algorithm.  This study utilizes principal component analysis (PCA) as a means of extracting optimized features from the dataset, employing techniques for dimension reduction prior to modeling the data. Result: Cumulative explication of the concept of variance constitutes a foundational aspect of PCA (principal component analysis) within the scope of the current research, namely a dimensionality reduction technique employed in multivariate data analysis to facilitate model development, thereby enabling the creation of more optimal and comprehensive models. In this research, the dimensions of training data are incorporated during the process of model creation.   The results show KNN model exhibits the highest performance, with an accuracy of 86%, precision of 86%, recall of 91%, and F1-score of 88%. Furthermore, evaluation using the ROC metric also provides a relatively favorable value, 0.85. Novelty: Researchers used 1190 patient data sourced from Kaggle. Before modeling the algorithm, researchers conducted EDA & Preprocessing which includes missing values to find data that does not have information, then duplicate data to find duplicated data, there are 270 duplicated data, then the duplicated data is deleted so that the data becomes 737, then PCA implementation is carried out.  PCA is reducing features automatically without changing the data.
Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods Basuni, Nursela; Amril Mutoi Siregar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5401

Abstract

Drug abuse are on the rise, with many users enter the addiction phase, often resulting in overdose and death. Drugs are chemical compounds that are capable of affecting biological functions, and they can induce feelings of happiness and reduce pain. To address this growing problem, a proactive measure is needed. Therefore, this study aims to classify drug users and non-users, so that health workers and therapists can educate about the dangers of drugs to non-users and rehabilitate drug users. This study uses drug consumption data taken from the UCI Irvine Machine Learning Repository. The data consist of 1885 rows with 32 attributes and 2 classes, where there are 18 types of legal and illegal drugs. This research utilizes machine learning methods, specifically Artificial Neural Networks (ANN), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF), in addition to evaluation methods such as Confusion Matrix and Area Under Curve (AUC). The results showed that RF outperformed the other methods, with accuracy, precision, and recall of 93%, and an f1 score of 89%, while the AUC value was still suboptimal at 0.66. DT had the worst results, with 82% precision, 87% precision, 82% recall, 84% f1 score, and an AUC value of 0.56. With these results, this research can be continued into an application that can classify drug users and nonusers.
AI-AUGMENTED HEALTH INFORMATION SYSTEM DESIGN FOR SCALABLE MOBILE MENTAL WELLNESS SUPPORT IN INDONESIA Tiawan; Eliza Ariesta; Nilam Atsirina Krisnaputri; Amril Mutoi Siregar; Surjandy; Merios Gusan Putra; Dani Lukman Hakim; Samsul Arifin; Nur Dava Kurniawan
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.6974

Abstract

Indonesia faces a youth mental health crisis, with limited access to professional care. WHO recommends one mental health worker per 30.000 people, but Indonesia only has 1:200.000 for general mental health personnel and 1:223.587 for psychiatrists. This paper addresses the implementation gap through the design and integration of an AI augmented health information system within Teman Curhat, a mobile mental wellness platform. We applied thematic analysis to expert interviews and used K-Means clustering on 16.689+ user reported cases to classify concerns into three severity levels. The system’s novelty lies in embedding on device Gemini AI for offline triage, ethical chatbot support, and adaptive user routing. Key outputs include a validated clustering model (Silhouette Score: 0.61), AI assisted classification logic, integration within Android Studio, and a scalable conversational interface. This innovation supports SDG 3 (Good Health and Well being) and SDG 10 (Reduced Inequalities) by enabling inclusive, responsive, and privacy focused digital mental health services for underserved communities.
MODEL CHATBOT AI AGENT BERBASIS CONVERSATIONAL AGENT UNTUK BISNIS DIGITAL LAPTOPCARE Tiawan; Eliza Ariesta; Nilam Atsirina Krisnaputri; Amril Mutoi Siregar; Surjandy; Merios Gusan Putra; Dani Lukman Hakim; Samsul Arifin; Muhammad Fathir Fahlevi; Favian Jarsi Taufiqqurakhman; Hexsel Aldoegasha; Alya Nabilah; Taufiqqurahman Hutri; Nur Davi Kurniawan
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.6975

Abstract

Perkembangan teknologi Conversational Agent (CA) memungkinkan pengembangan chatbot berbasis kecerdasan buatan untuk mendukung layanan bisnis digital. Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan chatbot AI Agent sebagai layanan dukungan pelanggan pada model bisnis perawatan laptop yang dikembangkan oleh mahasiswa. Metode yang digunakan adalah pendekatan Conversational Agent dengan integrasi n8n sebagai platform otomasi alur kerja, Telegram sebagai saluran komunikasi, Google Gemini Chat Model sebagai pemroses bahasa, serta Google Spreadsheet sebagai sumber data terstruktur untuk informasi produk dan layanan. Sistem chatbot dirancang sebagai agen berbasis tugas dengan aturan bisnis yang mengatur jenis pertanyaan dan layanan yang dapat diberikan. Hasil penelitian menunjukkan bahwa chatbot mampu memberikan respons yang akurat, kontekstual, dan konsisten terkait informasi layanan, stok barang, estimasi harga, serta kebutuhan pelanggan secara real-time. Implementasi ini membuktikan bahwa integrasi AI Agent dengan platform otomasi dan data terstruktur dapat meningkatkan efisiensi layanan pelanggan serta mendukung pengembangan model bisnis digital di bidang perawatan laptop.
PERANCANGAN FITUR DARURAT DAN PENGINGAT PADA APLIKASI JAMAAH UMROH XYZ MENGGUNAKAN SCRUMBAN Tiawan; Nusaibah; Rifky Kurniawan; Eliza Ariesta; Nilam Atsirina Krisnaputri; Amril Mutoi Siregar; Surjandy; Merios Gusan Putra; Dani Lukman Hakim; Samsul Arifin; Citra Nur Napiah
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.7049

Abstract

The problem of Umrah pilgrims getting lost and losing personal belongings is still common, especially in high-density areas such as the Grand Mosque and the Prophet's Mosque, with more than 50 cases of missing pilgrims per day during the peak Umrah season. A survey of 50 pilgrims showed that 86% of respondents experienced location disorientation and 74% had lost or nearly lost personal belongings. This study aims to design emergency and reminder features in the XYZ Umrah Pilgrim Application to improve security and management of pilgrim activities. The development method used is Scrumban, a combination of the iterative and structured Scrum method with the Kanban method that emphasizes workflow visualization and task management execution. The emergency feature is designed using the Fused Location Provider to send pilgrim location coordinates in real-time with a detection time of less than 30 seconds, while the reminder feature helps manage luggage and prayer schedules through a notification system. Functional and performance testing results show a feature success rate above 90%. The conclusion of this study shows that the integration of emergency and reminder features in one Umrah application with the Scrumban approach is able to improve the security and comfort aspects of the congregation.
Co-Authors Abda Abda Abdul Mufti Ahmad Fauzi Ahmad Fauzi Alma Hidayanti Alya Nabilah Andri Juliyanto Anton Romadoni Junior Aprilia, Ely Ariesta, Eliza ARIF, SITI NOVIANTI NURAINI Aulia, Achmad Indra Baihaqi, Kiki Ahmad Basuni, Nursela Bunga Tiara, Vira Citra Nur Napiah Deden Wahiddin Dwi Sulistya Kusumaningrum Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Faisal, Sutan Fariz Duta Nugraha Farkhina Dwi Utari Fauzi Ahmad Muda Favian Jarsi Taufiqqurakhman Fitri Nur Masruriyah, Anis Goeirmanto, Leonard Hanny Hikmayanti Handayani Hartono Wijaya, Sony Hexsel Aldoegasha Hilda Yulia Novita Indi Nurul Hassanah Indra Maulana` Indra Maulana Indra, Jamaludin Jaman, Jajam Haerul Jayidan, Zirji Juwita, Ayu Ratna Koirunnisa, Koirunnisa Kurniawan, Ade kurniawan, Rifky Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Lestari, Santi Arum Puspita Lilis Kartika Lutfiah Adeliana Maulana Abdur Rofik Maulana, Ikhsan Muhammad Fathir Fahlevi Mulya Cahya Ramadanty Murniasih nabila, putri Nahrowi Nahrowi Nahrowi Nilam Atsirina Krisnaputri Nofita Sari Nur Dava Kurniawan Nur Davi Kurniawan Nusaibah Nusaibah Permana, Tedi Pratama, Adi Rizky Priyatna, Bayu Rahmad Nahar Siregar Rahmat Rahmat Ramadhan, Naufal Cahya Rizqi Fahrozi Rohana, Tatang Romadoni, Nurul Salsa Desmalia Samsul Arifin Santi Arum Puspita Lestari Sekar Wuni Sinta Candra Dewi Sinung Suakanto SITI NURJANAH Siti Silvia Arifin Sony Hartono Wijaya Sony Hartono Wijaya Sukamto, Ika Sumiyarsi Surjandy Sutan Faisal Sutan Faisal Sutan Faisal Tatang Rohana Taufiqqurahman Hutri Tia Astiyah Hasan Tiawan Tjong Wan Sen Tjong Wan Sen Tohirin Al Mudzakir Tohirin Al Mudzakir Tria Pratiwi Sutriyani Tukino Tukino Tukino, Tukino Wilda Amalia Y Aris Purwanto Yana Cahyana Yana Cahyana Yana Cahyana Cahyana Yholanda Maldini Yogi Firman Alfiansyah Yusuf Khoiruddin