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All Journal JOIN (Jurnal Online Informatika) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Bulletin of Computer Science Research Journal of Informatics Management and Information Technology Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Media Journal of General Computer Science (MJGCS) The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat Unama
Jasmir
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Optimasi Klasifikasi Gestur Tangan Menggunakan Metode CNN Dengan Implementasi Strategi Landmark Berbasis Warna Komplementer Agus Nugroho; Jasmir; M. Riza Pahlevi. B, S; Roby Setiawan
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): 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.v14i2.4645

Abstract

The growth of hand gesture recognition technology has positively impacted various sectors. However, classification errors often occur due to the similarity of gesture shapes, which are challenging for models to differentiate. This study aims to develop a classification method based on Convolutional Neural Network (CNN) using a landmark modification approach with complementary colors. This approach applies significant color contrast to enhance the model’s ability to extract unique features from similar hand gestures. The dataset used includes gestures with color modifications on landmarks using an HSV-based color wheel to create maximum contrast. The data is then processed through bounding box creation, resizing, and transfer learning using the Teachable Machine architecture. The study results show a significant improvement in classification accuracy for models with landmark modifications compared to those without. Metrics analysis, including precision, recall, and F1-score, confirms that this approach effectively reduces classification errors caused by similar hand gestures.
Peningkatan Kinerja Klasifikasi Naive Bayes dengan Fitur Adaboost pada Penyakit Diabetes Rohaini, Eni; Nurhayati; Pahlevi, Riza; Gunardi; Jasmir
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 4 No. 4 (2025): EDISI JULI 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v4i4.11194

Abstract

Diabetes adalah gangguan metabolisme kronis yang ditandai dengan tingginya kadar gula (glukosa) dalam darah. Diabetes memerlukan perhatian dan manajemen yang berkelanjutan untuk mencegah komplikasi yang dapat memengaruhi kualitas hidup penderita. Dalam menghadapi kondisi ini, deteksi dini diabetes menjadi krusial untuk mencegah komplikasi yang lebih serius. Pemanfaatan teknologi dianggap sebagai solusi untuk mengurangi kesalahan estimasi. Dalam penelitian ini, digunakan metode Naïve Bayes berkolaborasi dengan fitur Adaptive Boosting (AdaBoost). Tujuan dari penelitian ini adalah untuk meningkatkan kinerja metode klasifikasi Naive Bayes dengan menggunakan fitur AdaBoost. Dataset yang digunakan diambil dari situs Kaggle. Temuan menunjukkan bahwa fitur Adaboost mempu meningkatkan nilai evaluasi kinerja klasifikasi naive bayes. Algoritma naïve bayes mengalami peningkatan akurasi sebesar 4,94%, presisi sebesar 4,22%, recall sebesar 6,6% dan f1-score 5,42%.
Design of a Decision Support System for Scholarship Award Recommendations Using the TOPSIS Method (Case Study: MTS Al-Hidayah, Jambi City) Ramdani Saputra; Jasmir; Dodi Sandra
Media Journal of General Computer Science Vol. 2 No. 2 (2025): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v2i2.104

Abstract

Processing scholarship data at MTS Al-Hidayah Kota Jambi takes a relatively long time in determining who is eligible to receive the scholarship and in recording student data there are often errors in inputting student identities so that a scholarship decision support system is needed using PHP programming languages and MySQL DBMS. The author develops the system using the waterfall method and uses the unified model language system model approach using use case diagrams, activity diagrams, class diagrams and flowchart documents. The results of the system can display student assessment data and the results of the calculation of scholarship awarding with the TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution)
Analisis Perbandingan Algoritma K-Means Dan K-Medoids Dalam Mengukur Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Saputra, Sahril; Kurniabudi; Jasmir
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 2 (2025): JAKAKOM Vol 5 No 2 SEPTEMBER 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.2.2292

Abstract

This study aims to analyze the comparison of K-Means and K-Medoids algorithms in measuring the level of student satisfaction with academic services at the Islamic Institute of Mamba'ul Ulum Jambi. Student satisfaction data were collected through questionnaires and analyzed using both algorithms with the help of RapidMiner tools. Clustering results were evaluated using the Davies Bouldin Index (DBI) to determine the most optimal algorithm. The results showed that most students at the Islamic Institute of Mamba'ul Ulum Jambi were very satisfied with the academic services provided. Clustering with K-Means and K-Medoids successfully grouped students into three clusters: "Very Satisfied", "Satisfied", and "Unsatisfied". The K-Means algorithm produced clusters with 450 members ("Very Satisfied"), 351 members ("Satisfied"), and 218 members ("Unsatisfied"). Meanwhile, K-Medoids produced clusters with 638 members ("Very Satisfied"), 270 members ("Satisfied"), and 111 members ("Unsatisfied"). Based on the DBI value, the K-Medoids algorithm (0.222) showed better performance than K-Means (0.396) in clustering student satisfaction data. This study has important implications for the Islamic Institute of Mamba'ul Ulum Jambi in evaluating and improving academic services
Implementasi Data Mining Dengan Menggunakan Algoritma FP-Growth Untuk Analisa Pola Peminjaman Buku Di Perpustakaan Universitas Jambi Wiratami, Latifa; Jasmir; Fachruddin
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 2 (2025): JAKAKOM Vol 5 No 2 SEPTEMBER 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.2.2295

Abstract

This study analyzes book borrowing transaction data from the Jambi University Library to identify borrowing patterns and extract valuable insights. By utilizing the FP-Growth algorithm within the framework of association rules, the research aims to uncover frequent itemset patterns that reveal relationships between different categories of borrowed books. These patterns are crucial for supporting librarians in making informed decisions for effective library management. The dataset consists of 2,978 book borrowing transactions recorded in 2022. Using Python for computational analysis, the study identified 14 association rules by applying a minimum support threshold of 0.005 and a minimum confidence threshold of 0.1. The resulting association rules include the following pairs: Management and Economics (0.006), Agriculture and Economics (0.014), Psychology and Education (0.013), General Works and Education (0.026), Mathematics and Education (0.005), Mathematics and Science (0.006), Mathematics and Economics (0.006), Social Sciences and Law (0.007), Politics and Law (0.012), Politics and Social Sciences (0.005), and Fiction and Language (0.005). These association rules offer valuable insights that can assist librarians in optimizing the organization of book collections, prioritizing acquisitions, and making strategic decisions to enhance the quality of library services. This approach highlights the potential of data-driven decision-making to improve library operations and increase user satisfaction.
Perbandingan Algoritma Naïve Bayes Dan Regresi Logistik Untuk Memprediksi Kesehatan Mental Mahasiswa Di Provinsi Jambi Fitriani; Jasmir; Sharipuddin
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 2 (2025): JAKAKOM Vol 5 No 2 SEPTEMBER 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.2.2309

Abstract

he mental health of college students is a global concern due to the impact of academic pressure, social issues,and lifestyle changes. In Indonesia, around 20% of the population is estimated to suffer from mental disorders, with morethan 12 million people experiencing depression. The increase in suicide cases, including among college students in Jambi,shows the significant impact of stress on mental health. To address this issue, early prediction of mental health disorders is animportant step so that intervention can be carried out earlier. This study compared the accuracy of the Naïve Bayes algorithmand logistic regression in predicting the mental health of college students in Jambi Province. Data were collected from 300students at three different universities. The results showed that Naïve Bayes had an accuracy of 99.58% on the training setand 100% on the testing set, while logistic regression only reached 61.67% on the training set and 63.33% on the testing set.These results indicate that Naïve Bayes is superior to logistic regression in predicting the mental health of college students.These findings can be the basis for the development of more effective early detection tools, so that educational institutionscan design appropriate intervention strategies to support student well-being
Perancangan Dan Implementasi Sistem Penunjang Keputusan Penilaian Kinerja Dosen Di Universitas Muhammadiyah Jambi Menggunakan Metode Topsis Etriyanti, Siska; Jasmir; Benni Purnama
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 2 (2025): JAKAKOM Vol 5 No 2 SEPTEMBER 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.2.2340

Abstract

Faculty performance assessment is a crucial factor in maintaining and improving academic quality in higher education institutions, including Universitas Muhammadiyah Jambi. However, the current evaluation methods are still subjective and manual, making them susceptible to bias and inefficiency. Therefore, this study aims to design and implement a Decision Support System for faculty performance evaluation using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. This method allows faculty ranking based on predefined criteria, such as teaching, research, community service, and other supporting elements. The system is designed to make the evaluation process more objective, transparent, and efficient. Implementation results indicate that the developed system can reduce subjectivity in assessments, enhance decision-making accuracy, and accelerate the faculty performance evaluation process. Thus, this system is expected to be an innovative solution for Universitas Muhammadiyah Jambi in improving academic quality and supporting faculty professional development. Keywords: Faculty performance evaluation, Decision Support System, TOPSIS, Universitas Muhammadiyah Jambi
Analisa Dan Perancangan Mobile APP Unit Pengaduan Masyarakat Pada Dinas Perindustrian Dan Perdagangan Provinsi Sumatera Utara takhir, Said; Jasmir; Fachruddin
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 2 (2025): JAKAKOM Vol 5 No 2 SEPTEMBER 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.2.2366

Abstract

Abstract− The public complaint system is an important medium in accommodating aspirations, criticisms, and complaints from the public regarding public services. However, the complaint process at the Industry and Trade Service of North Sumatra Province is still carried out manually, making it less effective and efficient in terms of recording, monitoring, and following up on complaints. This study aims to analyze the needs and design an Android-based mobile application system for the Public Complaints Unit to facilitate the process of submitting and handling complaints. The system development method used is Waterfall, with design tools in the form of UML (Unified Modeling Language) such as use case diagrams, activity diagrams, and class diagrams. The results of this study are a complaint application design that can help improve the quality of public services by providing a structured complaint flow, documented complaint data storage, and follow-up notifications to users.
Perbandingan Algoritma Naive Bayes Dan K-Nearest Neighbor Pada Klasifikasi Email Muhammad Athorik Alfayyed; Dodi Sandra; Jasmir
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 2 (2025): JAKAKOM Vol 5 No 2 SEPTEMBER 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.2.2378

Abstract

Penelitian ini membandingkan algoritma Naive Bayes dan K-Nearest Neighbor (KNN) dalam klasifikasi email menggunakan fitur TF-IDF. Dataset terdiri atas 5.572 email yang diproses melalui pelatihan dan evaluasi model dengan validasi silang 5-fold. Metode evaluasi mencakup akurasi, precision, recall, F1-score, dan ROC/AUC. Hasil menunjukkan Naive Bayes unggul dengan akurasi 98,38% dan AUC 0,98 dibandingkan KNN yang mencapai akurasi 92,47% dan AUC 0,82. Naive Bayes lebih stabil dalam recall pada kelas spam (89%), sementara KNN memiliki precision sempurna (100%) namun rendah recall (44%).
Klasifikasi Penyakit Jantung Dengan Menggunakan Algoritma C5.O Nada Aprillia; arvita, Yulia; Jasmir
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 2 (2025): JAKAKOM Vol 5 No 2 SEPTEMBER 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.2.2383

Abstract

Penyakit jantung adalah gangguan yang terjadi pada sistem pembuluh pembuluh darah besar yang menyebabkan organ vital makhluk hidup tidak berfungsi dengan baik. Seiring berkembangnya ilmu pengetahuan, kasus tentang prediksi penderita penyakit jantung dapat diselesaikan menggunakan teknik data mining. Dalam proses diagnosa masalah yang sering kali terjadi adalah kurangnya akurasi pada proses klasifikasi. Untuk mengukur tingkat akurasi pada dataset dapat dilakukan dengan teknik klasifikasi. Penelitian ini akan melakukan klasifikasi pada dataset penyakit jantung yang diperoleh dari situs Kaggle dengan mengetahui kombinasi setiap atribut pada dataset dan kemudian melakukan perhitungan untuk mengetahui tingkat akurasi pada dataset menggunakan teknik klasifikasi data mining dengan menggunakan algoritma C5.0. Hasil tingkat akurasi prediksi algoritma C5.0 menggunakan tools RapidMiner pada data penyakit jantung dengan jumlah 918 data yang mempunyai 12 atribut, yaitu Penderita Penyakit Jantung (Heart Disease), Usia (Age), Jenis Kelamin (Sex), Jenis Sakit Dada (Chest Pain Type), Tekanan Darah Saat Istirahat (Resting Blood Pressure), Kolesterol (Cholesterol), Gula Darah (Fasting Blood Sugar), Hasil Elektrokardiografi Saat Istirahat (Resting ECG), Detak Jantung Maksimum (Max Heart Rate), Latihan Diinduksi Angina (exercise angina), oldpeak, ST Slope.
Co-Authors Abdul Haris Abdul Haris Afrizal Nehemia Toscany agus Nugroho Agus Nugroho, Agus Ali, Edi Amroni Arvita, Yulia Aryani, Lies Aulia, Dinda Bella Putri Cahyani Chindra Saputra Despita Meisak Devi Dodi Sandra Etriyanti, Siska Evan Alber Fachruddin Faisal Amir Fitriani Gunardi Gustirani, Amalinda Hetty Rohayani Husaein, Ahmad Ibnu Sani Wijaya Ibnu Sani Wijaya ISW Ilham Tri Maulana Jeperson Hutahaean Joni Devitra Jumaryadi, Yuwan Junaidi, Feri Khairuldi Khatina Sari Kinanti, Raudatul Kurniabudi M. Riza Pahlevi. B, S M. Taufik Aufa Maichel Hardi MARDIANA Meisarah, Neisya Muhammad Aji Triatama Muhammad Athorik Alfayyed Muhammad Fahmi1, Said Mutawadiah Nada Aprillia Nazrul Azizi Ni Luh Wiwik Sri Rahayu Ginantra Nopi Desmulyani Noviana, Risma Dwi NOVIANTO, YUDI Nurhayati Nurhayati OSCARIO Pardamean Simanjuntak, Daniel Sintong Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Pramudya, Yuga Pressa Persana Surya Saputra Purnama, Benni Putri, Della Utami Ramdani Saputra Rianti Agustini, Silvia Ridho Riza Pahlevi, Riza ROBY SETIAWAN Rohaini, Eni Samsul Lutfi Sani Wijaya, Ibnu saputra, sahril Sharipuddin SIKA, XAVERIUS takhir, Said Wahyu Hardian, Reza WILLY RIYADI Wiratami, Latifa Yudi Yudi Novianto Yulia Arvita