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All Journal Techno.Com: Jurnal Teknologi Informasi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Simantec Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Edukasi: Jurnal Pendidikan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOISIE (Journal Of Information Systems And Informatics Engineering) JTIK (Jurnal Teknik Informatika Kaputama) Seandanan: Jurnal Pengabdian pada Masyarakat Archive: Jurnal Pengabdian Kepada Masyarakat Journal of Applied Business and Technology SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science Jurnal Testing dan Implementasi Sistem Informasi Jurnal Pustaka Robot Sister International Journal of Mechanical, Industrial and Control Systems Engineering INOVTEK Polbeng - Seri Informatika
Alyauma Hajjah
Institut Bisnis Dan Teknologi Pelita Indonesia, Riau, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Social Sciences

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Penerapan Algoritma Naive Bayes Untuk Rekomendasi Genset Wijaya, Chandra; Hajjah, Alyauma
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 7 No. 1 (2023): Volume 7, Nomor 1, Januari 2023
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v7i1.32

Abstract

Pada saat ini, teknologi merupakan penggerak penting bagi suatu negara. Perkembangan teknologi di bidang ekonomi ditandai dengan munculnya online shop. PT. Yanmarindo Perkasa merupakan perusahaan yang bergerak di bidang jual beli mesin, perkakas, sparepart dan lain- lain. Barang yang dijual sangat beragam menyebabkan bingungnya konsumen dalam memilih barang yang ingin dibeli karena barang dengan jenis yang sama memiliki tipe berbeda. Untuk mengatasi hal tersebut, dibutuhkan suatu sistem rekomendasi untuk memberikan rekomendasi barang yang sesuai dengan kriteria dan kebutuhan konsumen. Sistem rekomendasi ini dibangun menggunakan algoritma Naïve Bayes sebagai salah satu bagian dari data mining. Algoritma Naïve Bayes adalah metode klasifikasi yang didasarkan pada probabilitas dan statistik. Sistem rekomendasi ini terbatas pada mesin genset dimana data training yang digunakan yaitu kombinasi dari data spesifikasi dan data penjualan genset. Algoritma Naïve Bayes digunakan untuk mencari probabilitas terbesar dari seluruh instance pada atribut target seperti merek, bahan bakar, kapasitas, tegangan dan penyalaan. Dari salah satu contoh data uji yang dimasukkan, terdapat satu genset yang tampil sebagai hasil rekomendasi dari perhitungan algoritma Naïve Bayes. Berdasarkan hal tersebut, terbukti bahwa algoritma Naïve Bayes dapat digunakan untuk memberikan rekomendasi genset sesuai dengan kebutuhan dan kriteria konsumen. Dengan penerapan algoritma ini pada sistem rekomendasi genset, diharapkan dapat memberikan hasil yang akurat dan mengurangi kebimbangan konsumen dalam mencari genset.
Comparison of Distance Measurements Based on k-Numbers and Its Influence to Clustering Deny Jollyta; Prihandoko Prihandoko; Dadang Priyanto; Alyauma Hajjah; Yulvia Nora Marlim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3078

Abstract

Heuristic data requires appropriate clustering methods to avoid casting doubt on the information generated by the grouping process. Determining an optimal cluster choice from the results of grouping is still challenging. This study aimed to analyze the four numerical measurement formulas in light of the data patterns from categorical that are now accessible to give users of heuristic data recommendations for how to derive knowledge or information from the best clusters. The method used was clustering with four measurements: Euclidean, Canberra, Manhattan, and Dynamic Time Warping and Elbow approach for optimizing. The Elbow with Sum Square Error (SSE) is employed to calculate the optimal cluster. The number of test clusters ranges from k = 2 to k = 10. Student data from social media was used in testing to help students achieve higher GPAs. 300 completed questionnaires that were circulated and used to collect the data. The result of this study showed that the Manhattan Distance is the best numerical measurement with the largest SSE of 45.359 and optimal clustering at k = 5. The optimal cluster Manhattan generated was made up of students with GPAs above 3.00 and websites/ vlogs used as learning tools by the mathematics and computer department. Each cluster’s ability to create information can be impacted by the proximity of qualities caused by variations in the number of clusters.
Analisis Penerapan Augmented Reality Sebagai Strategi Pemasaran: Uji Black Box dan Korelasi Kody, Jeffry; Jollyta, Deny; Hajjah, Alyauma; Pratama, Teddy
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.v11i1.3037

Abstract

Traditional marketing no longer ensures greater revenue. Businesses in the advertising industry are also feeling the effects of this circumstance. People with a lot of mobility have less time to go shopping and visit stores. The demand for seeing product designs continues to rise, yet many people are unable to attend in person, resulting in greater time spent at work. Entrepreneurs must alter their marketing strategy to address these issues utilizing technology that is simple to use and available at all times. The goal of this research is to design an Augmented Reality (AR) application that can be used on a smartphone and can process sales via the internet. Black Box, light intensity, and the proper distance are used to create and test applications for functioning so that product photos seem at their best. The existence of the app also generates a strong correlation between customer interest of product and desire to purchase it. This is demonstrated by a correlation test with a value of 0.673191. It is envisaged that the designed application can aid advertising enterprises in enhancing marketing and sales.
HAND POSE CLASSIFICATION USING MEDIAPIPE HANDS AND CNN-LSTM FOR AUGMENTED REALITY BASED INTRAVENOUS INFUSION LEARNING Desnelita, Yenny; Siddik, Muhammad; Lita, Lita; Hajjah, Alyauma; Gustientiedina, Gustientiedina
Jurnal Testing dan Implementasi Sistem Informasi Vol. 3 No. 2 (2025): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v3i2.2343

Abstract

Intravenous infusion training requires precise hand positioning and coordinated movements; however, conventional training approaches remain subjective and lack consistent real-time feedback. Moreover, existing augmented reality (AR)-based systems are largely limited to visualization and do not provide intelligent, automated skill evaluation. To address this gap, this study proposes an integrated hand pose classification framework that combines MediaPipe-based landmark extraction, CNN-LSTM spatio-temporal modeling, and AR-based feedback for real-time procedural learning. The novelty of this work lies in the seamless integration of lightweight feature representation, hybrid deep learning, and interactive AR feedback within a unified learning system. Experimental results demonstrate that the proposed approach achieves high classification performance, with an accuracy of 94.82% and an AUC of approximately 0.97, indicating strong discriminative capability. The system also operates in real time with low latency, enabling immediate feedback and adaptive learning. This study contributes theoretically to spatio-temporal gesture modeling and practically to the development of intelligent AR-based training systems. The proposed framework offers a scalable and objective solution for improving procedural accuracy, consistency, and accessibility in medical education.
Professional Clustering Based on the Graduates Profile Using K-Means Method Susi Susi; Alyauma Hajjah
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 9 No. 1 (2021): Maret 2021
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v9i1.2264

Abstract

The Information Systems department of Pelita Indonesia Institute of Business and Technology produces graduates with education knowledge to face the professional work, but the majority of students after graduating do not work according to their graduate profiles/educational background. This research aimed to help students in determining the appropriate graduate profile. The proposed system was built using the K-means method for the classification process of graduate profiles; hence, the results can be used as recommended profession to be taken. The data used in this study is the data of students of class 2016, and these results are compared with their current professional record data with the aim of knowing the percentage of professional suitability obtained with the current profession. After this research is tested, the results of the classification of the graduate profile can be obtained where there are 10 students in the administrator database cluster, 11 students of the web design and developer cluster, one student of the cluster information system manager, and 8 students of the cluster system analysis. The percentage of suitability was 43.33%. This program is designed using the PHP programming language.
HAND POSE CLASSIFICATION USING MEDIAPIPE HANDS AND CNN-LSTM FOR AUGMENTED REALITY BASED INTRAVENOUS INFUSION LEARNING Yenny Desnelita; Muhammad Siddik; Lita Lita; Alyauma Hajjah; Gustientiedina Gustientiedina
Jurnal Testing dan Implementasi Sistem Informasi Vol. 3 No. 2 (2025): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v3i2.2343

Abstract

Intravenous infusion training requires precise hand positioning and coordinated movements; however, conventional training approaches remain subjective and lack consistent real-time feedback. Moreover, existing augmented reality (AR)-based systems are largely limited to visualization and do not provide intelligent, automated skill evaluation. To address this gap, this study proposes an integrated hand pose classification framework that combines MediaPipe-based landmark extraction, CNN-LSTM spatio-temporal modeling, and AR-based feedback for real-time procedural learning. The novelty of this work lies in the seamless integration of lightweight feature representation, hybrid deep learning, and interactive AR feedback within a unified learning system. Experimental results demonstrate that the proposed approach achieves high classification performance, with an accuracy of 94.82% and an AUC of approximately 0.97, indicating strong discriminative capability. The system also operates in real time with low latency, enabling immediate feedback and adaptive learning. This study contributes theoretically to spatio-temporal gesture modeling and practically to the development of intelligent AR-based training systems. The proposed framework offers a scalable and objective solution for improving procedural accuracy, consistency, and accessibility in medical education.
Implementation of Certainty Factor Method in Mental Health Diagnosis Expert System in Adolescents Aged 18 – 24 Years Yenny Desnelita; Mario Cesar; Gustientiedina Gustientiedina; Alyauma Hajjah; Ramalia Noratama Putri
Journal of Applied Business and Technology Vol. 6 No. 1 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i1.195

Abstract

Mental health in adolescents aged 18-24 years is a real condition and a problem that has received less attention from parents or certain parties. Individual adolescents free from all forms of symptoms of mental disorders are one of the keys to maintaining a healthy body from mental health disorders . The group most vulnerable to mental health disorders is adolescents, where many adolescents do not receive the care they should from their parents. Expert systems can help solve problems in the field of mental health in adolescents aged 14-18 years as befits a psychiatrist by adopting expert knowledge into computers. This study aims to develop an expert system for diagnosing mental health disorders in adolescents aged 18-24 years using the Certainty Factor (CF) method by combining expert and user belief values in the diagnostic solutions provided later by the expert system. This study used five mental disorders in adolescents aged 18-24 years, namely depression, schizophrenia, bipolar, obsessive, anxiety disorders which were later given the weight of symptom beliefs and data on preventive solutions to the disease using the CF method . The results of the study are in the form of an expert system for diagnosing mental health in adolescents. using the CF method which displays the certainty value of expert knowledge diagnosis. Testing of the expert system application in this study uses the black-box method with valid test results used.
Soursop Leaf Disease Detection With CNNs:   From Training to Deployment Siti Hidayatullah Nuriadi; Erlin Sabri; Alyauma Hajjah; Ramalia Noratama Putri
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/sn8avr92

Abstract

Soursop (Annona muricata) is a valuable tropical fruit crop that is highly susceptible to leaf diseases caused by fungal, bacterial, and viral infections. These diseases can significantly impact crop yield and quality, posing challenges for farmers, especially when early detection is delayed. This study proposes an automated solution using Convolutional Neural Networks (CNNs) to detect soursop leaf diseases through image classification. A dataset of 400 labelled leaf images, including healthy and diseased leaves (Leaf Rust, Leaf Spot, and Sooty Mold), was collected and preprocessed for the dataset. Three CNN architectures—MobileNetV2, VGG19, and ResNet50—were evaluated based on accuracy, precision, recall, and F1-score. Among them, MobileNetV2 outperformed the others, achieving 73% accuracy, 72% precision, 65% recall, and 66% F1-score and demonstrated strong consistency across classes. The best-performing model was deployed using the Flask web framework, enabling users to upload soursop leaf images and receive instant disease classification along with suggested treatments and preventive measures. This study’s novelty lies in the end-to-end pipeline, from model training to deployment via Flask, providing a ready-to-use solution for farmers.
A Fault Diagnosis and Intelligent Monitoring Framework Using Explainable Artificial Intelligence for Smart Industrial Machinery Siska Nar; Ahmad Nugroho; Ahmad Subhan Yazid; Helmi Wibowo; Alyauma Hajjah
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 4 (2025): December :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i4.405

Abstract

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.
Enhancement of Supervised Learning Models for Intrusion Detection Through Mutual Information and Hyperparameter Tuning Deny Jollyta; Yoakhina Nicole Makaruku; Alyauma Hajjah; Yulvia Nora Marlim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5760

Abstract

Enhancing the performance of supervised learning algorithms through feature and hyperparameter testing remains challenging for users, particularly when detecting computer network intrusions. There are opportunities to assess whether a supervised learning algorithm performs optimally, depending on the number of features and the choice of hyperparameters. The purpose of this research is to enhance the network intrusion detection performance of three supervised learning algorithms, namely Support Vector Machine (SVM), eXtreme Gradient Boosting, and Random Forest, by using the Mutual Information feature selection approach and hyperparameter tuning. Mutual Information measures the dependency of features on the target. Features with high values are the most informative. Hyperparameters are not learned from the data; they are set before training begins. Hyperparameters are selected in accordance with the requirements of the three algorithms via iterative training and testing on the NSL-KDD dataset. The dataset was split into 80:20, 70:30, and 60:40. The results showed that the fifteen features with the highest mutual information were identified and trained on the data using appropriate hyperparameters. By splitting the data in an 80:20 ratio, the accuracy of Support Vector Machine reached its maximum, increasing from 90% to 98%. In contrast, eXtreme Gradient Boosting and Random Forest reached their maximum, increasing from 97% and 98% to 100%, respectively. The study’s findings advance our understanding of how algorithm performance depends on feature and hyperparameter selection.
Co-Authors Achmad Tavip Junaedi Adiya, M. Hasmil Agustin ahmad kamal, ahmad Ahmad Nugroho Ahmad Subhan Yazid Andra Andra Azridjal Aziz Cecilia Cecilia Chandra Wijaya Chandra Wijaya Chandra Wijaya Chandra Wijaya Dadang Priyanto Darmanta Sukrianto Deny Jollyta Deny Jollyta Desnelita, Yenny Dewi Nasien Dwi Oktarina Dwi Oktarina Erlin Erlin Sabri Gusrianty Gusrianty Gusrio Tendra Gustientiedina Hadi Dwi Putra Helmi Wibowo Hendri Hartono, Hendri Hidayatullah Nuriadi, Siti Hutri Rizkiyah Alda Jahrizal Jeffry Kody Johan Johan Johan Johan Johnny Anwar Angkasa Josef Sianturi Junita Cahyati Sinaga Kody, Jeffry Leonardo Yang Lita Lita Lita, Lita Loneli Costaner M Dalil Manurung, Marisah Elfrida Marcellino, Marcellino Mario Cesar Marlim, Yulvia Nora Muhammad Siddik Mukhsin Mukhsin Nicholas Renaldo Noratama Putri, Ramalia Nyoto Pratama, Teddy Prihandoko . Prihandoko Prihandoko Rahmadian Yuliendi, Rangga Ramalia Noratama Putri Ramalia Noratama Putri Renta Wasni Putri Sanjaya, Irfan Siddik, Muhammad Sihombing, Sagita Charolina siska monica Siska Nar Siti Hidayatullah Nuriadi Son, Viktorianus son Sulaiman Musa Susi Susi susi susi Teddy Pratama Wahyu Joni Kurniawan Wilda Susanti Yermias Duha Yoakhina Nicole Makaruku Yusrizal Yusrizal