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All Journal Teknika Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Vocatech : Vocational Education and Technology Journal Jurnal Vokasi JOURNAL OF INFORMATICS AND COMPUTER SCIENCE G-Tech : Jurnal Teknologi Terapan Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JINAV: Journal of Information and Visualization Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology ARRUS Journal of Engineering and Technology Instal : Jurnal Komputer Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Ilmu Komputer dan Informatika Algoritme Jurnal Mahasiswa Teknik Informatika TECHSI - Jurnal Teknik Informatika Jurnal Teknologi Terapan and Sains 4.0 MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Pengabdian Masyarakat Ilmu Komputer Journal Serambi Engineering (JSE) Bulletin of Engineering Science, Technology and Industry JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Lidya Rosnita
Universitas Malikussaleh
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Penerapan Sistem Deteksi Pengisian Ruang Parkir Kendaraan Roda 4 Menggunakan Metode Computer Vision Di Orbit Future Academy Lidya Rosnita; Sujacka Retno
TECHSI - Jurnal Teknik Informatika Vol. 15 No. 1 (2024)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v15i1.16142

Abstract

Keberadaan kecerdasan buatan (AI) telah mengubah lanskap teknologi dan membawa perubahan signifikan bagi kehidupan manusia. Di Indonesia terdapat perusahaan Orbit Future Academy (OFA) yang berfokus pada program Artificial Intelligence 4Jobs (AI 4JOBS). AI 4JOBS bertujuan untuk memperkuat kompetensi individudalam kecerdasan buatan (AI) sebagai persiapan untuk terjun ke dunia kerja yangterus berkembang. Program AI 4JOBS di OFA dirancang dengan beragam modulyang mencakup pemahamankonsep AI, keterampilan teknis, aspek etika profesi,dan kesiapan berkarir. Dalam penelitian ini berfokus pada" Penerapan Sistem Deteksi Pengisian Ruang Parkir Kendaraan Roda Menggunakan Metode Computer Vision".Untuk menyelesaikan tugas tersebut, sebuah website AI dibangun dengan memanfaatkan domain AI Computer Vision  dengan ruang warna HSV (Hue, Saturation, Value) dan library OpenCV adalah pendekatan yang umum digunakan dalam pengolahan citrauntuk membedakan kendaraan dari latar belakanguntukmengidentifikasitata dalam pengaturan parkir kendaraan roda 4.Melaluiprogram AI 4JOBS di OFA, peneliti berhasil memperoleh pengetahuan yang luastentangAIdanmengasahketerampilanteknisyangsangatdibutuhkandalammenghadapiperkembanganteknologiAI. Selainitu,programinijugamemberiwawasantentangetikaprofesidankesiapanberkarirdieraAI.
Sistem Presensi Mahasiswa Berbasis Pengenalan Wajah Real-Time dengan Deteksi Anti-Spoofing Menggunakan YOLOv8 dan ArcFace Fajar Satria; Defry Hamdhana; Lidya Rosnita
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9502

Abstract

Student attendance recording is an important aspect in supporting discipline and administrative order in academic environments. Manual attendance methods still have several limitations, such as potential fraud and inefficient recapitulation processes. This study aims to develop a real-time face recognition-based student attendance system by implementing the YOLOv8 algorithm for face detection and ArcFace for identity recognition, complemented with an anti-spoofing feature to prevent fraudulent attempts. The system is designed to detect faces directly, recognize registered student identities, and record attendance automatically. The main contribution of this study lies in the integration of YOLOv8-based face detection, ArcFace-based face recognition, and an anti-spoofing mechanism into a single unified real-time attendance system. Experimental results show that the system successfully recognizes all registered students with a 100% success rate. The YOLOv8 anti-spoofing model demonstrates excellent performance in distinguishing real and fake faces, achieving an mAP@0.5 value of 0.995 and an F1-score close to 1. The system is also able to record attendance time in real time according to the actual time and present attendance data systematically. Based on these results, the developed real-time face recognition attendance system is accurate, secure, and feasible to be implemented as an attendance solution in academic environments
PREDICTION OF SUSTAINABILITY OF FAMILY PLANNING PARTICIPANTS BASED ON DEMOGRAPHIC CHARACTERISTICS USING RANDOM FOREST Dara Fazila; Zahratul Fitri; Lidya Rosnita
Bulletin of Engineering Science, Technology and Industry Vol. 4 No. 3 (2026): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Family Planning (KB) is one of the government programs aimed at controlling population growth and improving family welfare. However, some Family Planning participants discontinued the use of contraceptives, which may affect the success of the program. Therefore, this study aims to develop a prediction system for the continuity of Family Planning participants in Cot Girek District using the Random Forest algorithm. The study used 1,000 Family Planning participant records containing demographic, socioeconomic, and contraceptive-related attributes. After the data preprocessing stage, 998 records were used to construct the prediction model. The research involved data preprocessing, Random Forest model construction, and model evaluation using Hold-Out Validation and 5-Fold Cross Validation. The results showed that the Age of the Youngest Child attribute had the highest Gini Gain value of 0.3486, indicating that it was the most influential factor in predicting the continuity of Family Planning participants. The model achieved an Accuracy of 93%, Precision of 93.33%, Recall of 96.18%, and F1-Score of 94.74%, while 5-Fold Cross Validation produced an average accuracy of 97.40% with a standard deviation of ±4.95%. In addition, Black Box Testing confirmed that all system functions are operated according to user requirements. These findings indicate that the Random Forest algorithm can effectively predict the continuity of Family Planning participants and can be used as a decision-support tool to assist Family Planning officers in monitoring and providing more targeted assistance to participants.
Sistem Pendeteksi Tingkat Kesegaran Daging Ayam pada Citra Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Android Rayhan Naturizal; Wahyu Fuadi; Lidya Rosnita
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp301-312

Abstract

This research develops a chicken meat freshness detection system based on image processing, implemented on an Android platform using the Convolutional Neural Network (CNN) method optimized with TensorFlow Lite. The system classifies chicken meat into three categories: fresh, less fresh, and rotten. The CNN model uses 32 filters to enhance feature extraction from the meat images. Testing on 30 samples, with each category tested 10 times, showed an accuracy of 90%, with 27 correct detections and 3 errors in the less fresh category. While the system effectively identifies fresh and rotten categories, there is a challenge in distinguishing the less fresh category due to its ambiguous visual characteristics. One limitation is the lack of a bounding box, causing the application to still provide detection results even when the scanned object is not chicken meat. This application is specifically designed to detect chicken meat pieces, so it is not recommended for use outside this context.
Water Quality Monitoring and Control System for Tilapia Cultivation Based on Internet of Things Lidya Rosnita; Muhammad Ikhwani; Hafizh Al Kautsar Aidilof; Salamah Salamah; Widia Hamsi; Haris Yunanda Rangkuti
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.566

Abstract

This research analyzes the quality of water for tilapia habitat which is a type of brackish water fish that is currently widely cultivated by pond farmers. This fish is the choice because of its flexibility regarding habitat. However, despite having flexibility in terms of habitat, each harvest of tilapia that lives in a different habitat will produce tilapia with different quantity and quality. Currently, many tilapia farmers still carry out the cultivation process using traditional methods using ponds. Kuala Kerto Village, Lapang District, North Aceh is one of the locations where many tilapia fish farmers use ponds as a habitat for this fish. Not infrequently, changes in natural conditions such as rain and floods have an impact on tilapia fish ponds in this village. Thus, crop yields are very varied, often even resulting in losses. One of the reasons for this is that there is still minimal use of technology in tilapia cultivation in this village. The design of a water quality monitoring and control system for IoT-based tilapia cultivation in this research was carried out to help the problems of tilapia pond farmers. Through this research, a tool was produced in the form of a prototype IoT device that can be used to monitor and control water quality in tilapia fish ponds. This device utilizes several sensors such as turbidity sensors, ammonia sensors, salinity sensors, pH sensors, and several other sensors as data takers which will later be transmitted and displayed via a web application. Research and development of this device uses the RD method, namely research and development.
Plagiarism Detection Application for Computer Science Student Theses Using Cosine Similarity and Rabin-Karp Taufik Habib Ansyari; Dahlan Abdullah; Lidya Rosnita
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.686

Abstract

Plagiarism detection is critical in maintaining academic integrity, particularly in higher education. This study focuses on developing a plagiarism detection application for Computer Science student theses. The application leverages the Cosine Similarity and Rabin-Karp algorithms to accurately and efficiently detect textual similarities. Developed using JavaScript, the application provides an intuitive interface and reliable performance, making it a practical tool for educational institutions. The application includes features allowing users to upload thesis documents, analyze textual content, and measure plagiarism levels by comparing them to an existing dataset. The Cosine Similarity algorithm measures the overall similarity between documents, while the Rabin-Karp algorithm focuses on identifying exact matches in phrases and sentences. The results demonstrate the efficacy of both algorithms. For titles, the Cosine Similarity algorithm achieved a 100% similarity rate for identical documents while detecting minor plagiarism with a similarity level of 5.86% for other documents. For abstracts, it achieved 100% similarity for the first document, 2.78% for the second document, and 8.37% for the third document. These findings highlight the algorithm's ability to detect exact matches and partial overlaps in textual content. The Rabin-Karp algorithm showed comparable performance, particularly in detecting phrase-level similarities. For titles, it recorded 100% similarity for identical documents, 11.42% for the second document, and 16.92% for the third document. For abstracts, the algorithm also achieved 100% similarity for the first document, 11.42% for the second document, and 16.81% for the third document. The study confirms that both algorithms complement each other in detecting different forms of plagiarism. The Cosine Similarity algorithm excels in identifying global patterns of similarity, while the Rabin-Karp algorithm is more suited for finding exact matches in specific phrases or sentences. This dual approach provides a comprehensive solution for detecting plagiarism in academic theses. The findings from this research are promising and highlight the potential of the application as a reliable tool for ensuring academic integrity. Future improvements could include expanding the dataset, enhancing the user interface, and integrating additional algorithms for cross-language plagiarism detection. This application contributes to academic honesty and is a valuable resource for educators, researchers, and students in combating plagiarism effectively. 
Performance Analysis Algorithm Classification and Regression Trees and Naive Bayes Based Particle Swarm Optimization for Credit Card Transaction Fraud Detection Rita Afridah; Munirul Ula; Lidya Rosnita
International Journal of Engineering, Science and Information Technology Vol 4, No 3 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i3.523

Abstract

With the advancement of technology, credit cards have become a popular tool for transactions, both physically and online, due to their ease of use and seamless integration with banking systems. However, with the increasing use of credit cards, the cases of fraud have also risen, resulting in financial losses for both cardholders and banks. To address this issue, effective and efficient credit card transaction fraud detection has become a top priority. Using machine learning algorithms is one of the techniques that can be employed to detect fraud in credit card transactions. The purpose of this research is to determine the performance and find the best method of the CART algorithm, Naive Bayes, and their combination with Particle Swarm Optimization (PSO) in detecting fraud in credit card transaction histories. The data used consists of 568,630 big data entries with parameters including id, V1-V28, amount, and class. The research results obtained are as follows: the accuracy of the Naive Bayes algorithm is 93.15%, precision is 94%, recall is 93%, and AUC is 0.99. For the CART algorithm, the accuracy is 99.96%, with precision and recall at 100%, and AUC at 1.00. Additionally, the Naive Bayes algorithm combined with PSO achieved an accuracy of 98.50%, precision and recall of 98%, and AUC of 1.00. Lastly, the CART algorithm combined with PSO reached an accuracy of 99.97%, with precision and recall at 100%, and AUC at 1.00. It can be concluded that the best method resulting from the tests conducted is the Classification and Regression Trees method combined with Particle Swarm Optimization.
Expert System For Diagnosis of Mental Health Disorders in Students Using Case-Based Reasoning Method With a Web-Based Positive Psychology Approach Udurta Bancin; Bustami Bustami; Lidya Rosnita
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.592

Abstract

Mental health issues among students have become a significant concern affecting their quality of life and academic performance. An effective expert system is needed to diagnose and provide appropriate interventions. This research develops a web-based expert system that utilizes the Case-Based Reasoning (CBR) method combined with a positive psychology approach to diagnose mental health disorders in students. The CBR method identifies similarities between new and previous cases, while the positive psychology approach focuses on individual strengths and potential for growth. The system integrates a database of student mental health cases and CBR algorithms to produce relevant diagnoses. This study investigates four types of mental health disorders: panic, anxiety, stress, and depression. The method used for data analysis is Case-Based Reasoning. The diagnosis results are based on calculations from symptom choices within the system, where each symptom has a weight. The highest similarity calculation obtained from past cases is used as a solution to address the problem. System testing, based on expert knowledge with 15 test data samples categorized by mental health disorders and 38 symptoms, achieved an accuracy rate of 85%.
Mobile Learning Application Tahsin Al-Quran Using Dynamic Time Warping Method Based on Adroid Wahidatunnisa Nasution; Munirul Ula; Lidya Rosnita
International Journal of Engineering, Science and Information Technology Vol 4, No 3 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i3.512

Abstract

This research aims to design and build an Android-based Quran tahsin learning mobile application using the Dynamic Time Warping (DTW) method. This application offers tajweed learning features and voice exercises to find out the readings of Al-Quran readers. The DTW method is used to analyze the similarity between the user's voice pattern and the reference voice pattern in the application. The research methods used include reference collection, direct observation, and literature study. The application is designed with a user-friendly interface and equipped with an accurate ability evaluation feature, so that users can find out their weaknesses and strengths in learning Qur'an tahsin. Based on the test results, out of 42 voice data tested, 38 data were successfully recognized correctly and 4 data had errors. The average accuracy rate of this application reached 90.47%. This application is designed to overcome some of the main problems in learning Quran tahsin: lack of understanding of basic tahsin techniques, lack of appropriate learning tools, difficulty in evaluating skills, and lack of motivation to learn. With this application, users can learn Quran tahsin more easily and effectively through interactive and varied methods. Evaluation of users' ability to recite Quranic verses can also be done accurately, so that users can know their strengths and weaknesses in tahsin learning. The implementation of this application is expected to make a significant contribution in improving the quality of Quran tahsin learning among the wider community.
Implementasi Metode WASPAS dalam Menentukan Bidang Keilmuan Perguruan Tinggi Berdasarkan Minat dan Bakat Siswa SMKN 1 Lhokseumawe Fauzi Irham Pulungan; Safwandi Safwandi; Lidya Rosnita
TEKNIKA Vol. 19 No. 2 (2025): Teknika Mei 2025
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15522494

Abstract

Pemilihan bidang keilmuan di perguruan tinggi merupakan keputusan penting bagi siswa. Penelitian ini menggunakan metode Weighted Aggregated Sum Product Assessment (WASPAS) dalam Sistem Pendukung Keputusan (SPK) untuk menentukan bidang keilmuan perguruan tinggi siswa SMKN 1 Lhokseumawe berdasarkan minat dan bakat yang diperoleh dari angket. WASPAS menggabungkan pendekatan penjumlahan dan perkalian untuk mengolah data dan menghasilkan rekomendasi ke bidang Sains dan Teknologi (SAINTEK) atau Sosial Humaniora (SOSHUM). Kriteria yang di gunakan sebanyak 20 kriteria dan kriteria yang di gunakan berupa 20 pernyataan dari angket minat dan bakat. Setiap kriteria diberi bobot, dan perhitungan nilai Qi digunakan sebagai dasar rekomendasi. Hasil pengujian menunjukkan bahwa sistem mampu memberikan rekomendasi yang sesuai, dengan 123 siswa (61%) direkomendasikan ke bidang SAINTEK dengan nilai Qi terbesar 0,95 dan 77 siswa (39%) ke bidang SOSHUM dengan nilai Qi terkecil 0,49. Penelitian ini membuktikan bahwa metode WASPAS efektif dalam memilih bidang keilmuan perguruan tinggi yang sesuai dengan minat dan bakat mereka, sehingga dapat menjadi acuan dalam pengambilan keputusan akademik.
Co-Authors Afif, Muhammad Athallah Aidilof, Hafizh Al Kausar Aidilof, Hafizh Al Kautsar Al Kautsar Aidilof, Hafizh Amelia, Ulva Amir Fauzi Armaya, Devira Yuda Asrianda Asrianda Asrianda Asrianda Aulia Rachman Aulia Rachman Azwir, Andrea Micola Azzahra Iskandar, Farah Bustami Bustami Dahlan Abdullah Dara Fazila Deassy Siska Defry Hamdhana Dela, Monisa Dian Putri, Yohana Efendi, Syahril Efendi, Syahril Elma Fitria Ananda Eva Darnila Eva Darnila Fachry Abda El Rahman Fadlisyah Fadlisyah Fajar Satria Fasdarsyah Fasdarsyah Fauzi Irham Pulungan Fidyatun Nisa Fuadi, Wahyu Furqan, Hafizul Habib Muharry Yusdartono Hafidh Rafif, Teuku Muhammad Hafizh Al Kautsar Aidilof Harahap, Ilham Taruna Harahap, Lina Mardiana Haris Yunanda Rangkuti Ikramina ikramina ikramina, Ikramina Jange, Beno Kurniawati Kurniawati Lina Mardiana Harahap Mara Wahyu Alamsyah Pane Micola Azwir, Andrea Muhammad Azhari Muhammad Azhari Muhammad Daud Muhammad Fajri Muhammad Fikry Muhammad Ikhwani Muhammad Muhammad Muhammad Reza Muhammad Zarlis Muhammad Zarlis, Muhammad Muharry Yusdartono, Habib Mukti Qamal Mulizar Mulizar Mulyadi, Rizki Mundirawati, Cut Munirul Ula Muzaffar Rigayatsyah Nanda Sitti Nurfebruary Naturizal, Rayhan Naza Amarianda Nur Ismiza Nurdin Nurfebruary, Nanda Sitti Nurhaliza Bin Aras Nurqamarina Nurul Aula Nurwijayanti Pasaribu, Hafni Maya Sari Pratiwi, Dinda Putri, Sri Raihan Rachmat Triandi Tjahjanto Rahma Fitria, Rahma Rahmadani Sari, Putri Dwi Rahmat Triandi Rangkuti, Haris Yunanda Rayhan Naturizal Rian Kelana Putra Rini Meiyanti Risawandi, Risawandi Rita Afridah Rizal Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal Tjut Adek Rizky Putra Fhonna Safriana Safriana Safwandi Safwandi Said Fadlan Anshari Salamah Salamah Samosir, Dini Kairiyah Saputri, Rifa Andriani Sasmita Sasmita Siti Maimunah Sujacka Retno Syahputra, M Oriza Taufik Habib Ansyari Udurta Bancin Ulva Ilyatin Wahidatunnisa Nasution Wahyu Fuadi Widia Hamsi Yesy Afrillia Yunanda Rangkuti, Haris Zahlul Fasya Zahratul Fitri Zalfie Ardian Zara Yunizar Zulfadli Zulfadli Zulfadli Zulfadli