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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 INOVTEK Polbeng - Seri Informatika
Alyauma Hajjah
Institut Bisnis Dan Teknologi Pelita Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences

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DETEKASI JAWABAN UJIAN KATEKUMEN MENGGUNAKAN METODE COSINE SIMILARITY BERBASIS ANDROID Son, Viktorianus son; Hajjah, Alyauma
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 8 No. 1 (2024): Volume 8, Nomor 1, Januari 2024
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v8i1.449

Abstract

Dalam ajaran Gereja katolik memilik salah satu peraturan untuk umat dan juga orang awam yang ingin pindah agama melalui beberapa tahapan untuk bisa benar-benar mengimani agama barunya nanti yang dinamakan pembelajaran Katekumen. Maka dalam hal ini Penulis melakukan penelitian pada salah satu Gereja katolik yang berada di pekabaru yakni Gereja Santa Maria A Fatima. Permasalahan yang terjadi dari pihak Gereja Santa Maria dalam proses pembelajaran katekumen yaitu pada saat ujian akhir dikarenakan proses pemeriksaan hasil ujian baik soal objektif masih menggunakan proses pemeriksaan masih manual. Tujuan dari penelitian ini adalah untuk membantu pengajar memberikan nilai yang objektif dengan mengunakan metode cosine similarity pada sistem agar dapat melakukan penilaian jawaban dengan membandingkan kunci jawaban pengajar dengan jawaban pelajar, dengan hal ini penelitian yang dilakukan untuk membuat sistem yang dapat membantu permasalan yang pemeriksaan yang masih secara manual. Dengan adanya sistem penerapan metode Cosine Similarity untuk mendeteksi jawaban ujian katekumen berbasis Android dengan tingkat akurasi 72% dari uji coba keberhasillan sistem yang dibuat sehingga proses pada aplikasi ujian katekumen dengan metode ini dapat diterapkan dan dapat memudahkan pengajar untuk melakukan ujian dan juga memudahkan dalam proses pemeriksaan hasil ujian para pelajar dengan efektif dan efesien.
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.
Enhancement of Supervised Learning Models for Intrusion Detection Through Mutual Information and Hyperparameter Tuning Jollyta, Deny; Makaruku, Yoakhina Nicole; Hajjah, Alyauma; Marlim, Yulvia Nora
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.
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.
Co-Authors Adiya, M. Hasmil Agustin ahmad kamal, ahmad Andra Andra Cesar, Mario Chandra Wijaya Chandra Wijaya Chandra Wijaya Chandra Wijaya Dadang Priyanto Darmanta Sukrianto Deny Jollyta Desnelita, Yenny Dewi Nasien Dwi Oktarina Dwi Oktarina Erlin Gusrianty Gusrianty Gusrio Tendra Gustientiedina Hadi Dwi Putra Hendri Hartono, Hendri Hidayatullah Nuriadi, Siti Hutri Rizkiyah Alda Irwan Irwan Jeffry Kody Johan Johan Johan Johan Johnny Anwar Angkasa Josef Sianturi Junita Cahyati Sinaga Kody, Jeffry Leonardo Yang Lita, Lita Loneli Costaner Makaruku, Yoakhina Nicole Manurung, Marisah Elfrida Marcellino, Marcellino Marlim, Yulvia Nora Mukhsin Mukhsin Noratama Putri, Ramalia Pratama, Teddy Prihandoko . Prihandoko Prihandoko Rahmadian Yuliendi, Rangga Ramalia Noratama Putri Renta Wasni Putri Sanjaya, Irfan Siddik, Muhammad Sihombing, Sagita Charolina siska monica Son, Viktorianus son susi susi Susi Susi Teddy Pratama Wahyu Joni Kurniawan Wilda Susanti Yermias Duha