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Muhammad Iqbal

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Author-ID : 456238

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Articles

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Model Predictive Analysis of Performance in Training and Course Institutions Using Naive Bayes and K-Means Clustering Eko Budianto; Muhammad Iqbal
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v3i1.68

The performance of course and training institutions (LKP) is a crucial factor in determining the quality of non-formal education in Indonesia. Performance assessments are currently conducted manually using the National Accreditation Board for Non-Formal Education (BAN-PNF) assessment instrument, which is time-consuming and prone to subjectivity. This research aims to develop a predictive analysis model for the performance of course and training institutions using a combination of the Naive Bayes and K-Means Clustering methods. The K-Means Clustering method is used to group institutions based on similar characteristics across key variables such as trainers, infrastructure, curriculum, management, and graduate outcomes. These clustering results are then used as additional features for the Naive Bayes classification model to predict performance categories (high, medium, or low). Testing of 150 institutions' data showed a predictive accuracy of 89.2%, with three main clusters representing high-, medium-, and low-performing institutions. This model has the potential to become a data-driven tool for governments and institutions to conduct performance evaluations quickly, objectively, and adaptively to changes in training data.

Classification of Student Activity Status Using Machine Learning Algorithms at Royal University Hermawan, Rudi; Muhammad Iqbal
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v3i1.69

Inactivity is a significant challenge that impacts academic performance, retention rates, and the operational effectiveness of higher education institutions. Royal University faces an urgent need to identify students at risk of becoming inactive early, so that academic interventions can be carried out appropriately and effectively. This study aims to develop a classification model for student inactivity status (Active or Passive) using a machine learning approach, by testing three main algorithms: Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The dataset used consists of 642 student entries, including academic information such as Grade Point Average (GPA), total credits taken, attendance percentage, number of courses per semester, and semester level. The methodology steps include data cleaning and transformation, splitting the dataset into 80% training data and 20% testing data using a random sampling method ( train_test_split with random_state = 42), model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results show that DT and SVM achieve the highest accuracy of 98.44%, with maximum precision in predicting active students, while RF excels in recall (0.96), making it more effective in detecting active students at risk of being missed. Feature importance analysis reveals that GPA and attendance are the most determining factors in predicting student active status, while the number of courses, credits taken, and semester level have a lower additional influence. The primary contribution of this research is the provision of an accurate and practically applicable classification model, enabling universities to conduct automated student monitoring, proactive academic interventions, and data-driven decision-making. Implementing this model in academic information systems can improve the effectiveness of advising programs, reduce the risk of student inactivity , and support efforts to improve retention and graduate quality. This research also emphasizes the importance of contextual features in improving prediction accuracy and provides insights that can be leveraged for the development of data-driven academic strategies

The Best Caregiver at SOS Children’s Villages Using a Decision Support System Muhammad Iqbal; Syahputri, Maulisa
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v3i1.67

This study focuses on the development and implementation of a Decision Support System (DSS) designed to determine the best caregiver at SOS Children’s Villages. The main objective is to enhance efficiency and objectivity in the decision-making process related to caregiver performance evaluation. The research methodology includes collecting caregiver performance data, analyzing organizational needs, and applying an appropriate decision-making model. The DSS developed in this study utilizes Artificial Intelligence (AI) techniques to process and analyze performance data, generate performance scores, and identify the best caregiver based on predetermined criteria. The results show that the implementation of the DSS improves the objectivity of performance evaluations and provides significant support in the decision-making process. With this system, the organization is expected to better identify and optimize the potential of each caregiver, thereby increasing productivity and strengthening the competitiveness of SOS Children’s Villages in Medan. The collected data is processed and evaluated using the Simple Additive Weighting (SAW) method. The results are presented in the form of rankings and scores for each caregiver, facilitating a more accurate and transparent decision-making process. This study is expected to contribute positively to improving the efficiency and effectiveness of human resource management at SOS Children’s Villages.

Use of Data Warehouse and Data Mining for Academic Data: A Case Study at a National University Muhammad Iqbal; Muhammad Hasyim As’ary
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 2 (2025): April: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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

Universities must optimize their information resources to enhance organizational performance and support strategic decision-making. However, academic data stored in multiple operational systems often remains fragmented and difficult to analyze comprehensively. This study aims to develop a data warehouse and apply data mining techniques to integrate and analyze academic data at the National University (UNAS), Jakarta. The data warehouse was designed using a star schema model, integrating academic records from various operational databases into a centralized repository. Mondrian and JPivot were utilized for multidimensional data presentation, while Classification-Based Association (CBA) and Association Rule techniques were applied to uncover hidden patterns within the data. The results show that the data warehouse significantly improves reporting efficiency, reducing processing time from one month to one day. Data mining analysis further revealed characteristic patterns among students in selecting specialization programs based on academic performance. These findings demonstrate that the integration of data warehousing and data mining supports more accurate reporting, informed decision-making, and data-driven academic planning.

Trend Analysis and Job Classification in the Field of Artificial Intelligence Using the Support Vector Machine (SVM) Method Helmy, Ahmad; Muhammad Iqbal
Journal of Computer Science and Research (JoCoSiR) Vol. 2 No. 3 (2024): July: Artificial Intelligence
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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

The rapid advancement of Artificial Intelligence (AI) has significantly transformed the global job landscape, creating new opportunities while redefining existing roles. This study aims to analyze emerging trends and classify job roles in the AI domain using the Support Vector Machine (SVM) method. A dataset was collected from various online job marketplaces and professional platforms to identify key skills, qualifications, and job categories associated with AI-related professions. The data preprocessing involved text normalization, feature extraction using TF-IDF, and classification modeling through SVM. The experimental results demonstrate that the SVM model achieved high accuracy in categorizing AI-related occupations into predefined job clusters, such as Data Scientist, Machine Learning Engineer, AI Researcher, and AI Product Manager. Furthermore, the trend analysis revealed a growing demand for AI professionals with strong interdisciplinary skills combining data analytics, programming, and domain expertise. These findings provide insights for educational institutions, job seekers, and policymakers to align skill development strategies with the evolving needs of the AI workforce.

Predicting Public Health Risks Based on Lifestyle Factors Using the Support Vector Machine Andri, Andri Ismail Sitepu; Muhammad Iqbal
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 3 (2025): July: Health Science Informatic
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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

Public health risks are often influenced by multiple lifestyle factors, such as age, diet, exercise, smoking, and alcohol consumption. This study aims to develop a predictive model for assessing individual health risks using the Support Vector Machine (SVM) algorithm. The dataset used consists of lifestyle attributes, including age, weight, height, exercise frequency, sleep duration, sugar intake, smoking habits, alcohol consumption, marital status, profession, and body mass index (BMI). The data were preprocessed through normalization and label encoding, followed by training and testing using a 70:30 data split. The SVM model employed the Radial Basis Function (RBF) kernel to capture non-linear relationships between variables. Experimental results show that the proposed SVM model achieved an accuracy of approximately 89%, demonstrating strong predictive capability. The confusion matrix analysis revealed that the model effectively distinguishes between high and low health risk categories, while the PCA visualization confirmed clear clustering of classified data. Moreover, the feature importance analysis indicated that age, smoking habits, BMI, and alcohol consumption were the most significant contributors to health risk prediction. Overall, the results suggest that the SVM algorithm is a robust and efficient approach for predicting public health risks based on lifestyle factors. This model can serve as a foundation for preventive health monitoring systems, providing valuable insights for promoting healthier lifestyles and supporting data-driven public health strategies.

Co-Authors A Fachrin, Suharni Abd. Kahar Abdul Karim Abdul Wahid Safar Abdul Wahid Safar Abdurahman Abdurrahman Abisa, Jotian ade rahman adidi Adisty Aulia Zahra Adjie Gery Ramadhan Admiral Admiral Afaf Fitriati Affan Safani Adam Afriana Afrimon Afrinald Rizhan Afthar Kautsar Agaphe Suluh Brahmantio Agatha Agung Indariansyah Agung Wardoyo Agusmadi Agusmadi Ahmad Aspiani Ahmad Fardan Ahmad Madany Ahmad Sulaiman Siregar Ahmad Wahyudi Zein AHMADI Ahmed Zaranggi Ar Ridho Akbar Fauzi Mutaqin Akbar Juliandi Akhmad Kastalani Akmal Pratama Harahap Alam Fahlevi Pranata Aldi Pratama Alex Siregar Amer Abdul Aziz Amir Fauzi Ammar Fauzi Andi Azra Indira Nur Andi Nur'Aini Andi Nur’ Aini Andika Andika Andika Firman Yudistira Andri, Andri Ismail Sitepu Andrianto, Aldo Zakaria Andy Hermawan Andysah Putera Utama Siahaan Angelus Defananda Angga Sukma Budi Darmawan Angga Trisnanto Ani Naryani Anisa Anisa ANISAH Annisa Zahra Anwar Hafidzi Anzas Ibezato Zalukhu Aprian syukri ali nasution Aprilia Hikari Tamira Jasmine Aprinelita Aprinelita Ardan Syukur Arfan Ari Sri Ulandari Arini Puspita Sari Ariya Agustin Ashabul Jannah Siregar Asmawati Achamad Asnia Zainudin Ati Kusmawati Atri Sri Ulandari Aulia Maghfirah Aulia Meidiati aulia, izzmi Avia, Imelda Ayu Lestari Azalika, Elsa Azlim Azlim Azman Ismail Bagas Alivia M Bahrudin, Muh. Bakti Sentosa Bambang Antoko Bayu Aji Nugroho Bayu Yoni Setyo Nugroho Bintang Johan Permana Bintang Timur Bobby Indra Utama Bramandityo Prabowo Bungsu Keumalasari Cory Vidiati Dadan Nur Ramadan Daniati Hi Arsyad Dara Ditha, Meylani Darli, Viny Volcherina Darmeli Nasution Deby Safitri Hasanah Delia Sariyulis Depi Anggraini DESI APRIANI, DESI desi Fitri Yani Sembiring Desmawan sholak Dessy Masliani Lubis Destikasari DEWI RAHMAWATI Dewi Sartika Dhea Savitri Dian Arfani Qirom Dian Indira Putri Sibarani Diana Dewi Sartika, Diana Dewi Dimas Armadansyah Dina Mardiana Dina Sonia Diza Aulia Kusnadi Edi Istiyono Eka Alzahra Eka De Patmonsela Liow Eko Budianto Ella Nurmaini Elvira Andriyani EM Yusuf Iis Emzia Fajri Erika Kristina Sibarani Erina Riski Dwi Nurcahyani Erlisa Saraswati Ernawarnelis Ersari Espa , Vitriyan Eti Rimawati Eva Herlina Evi Sribudiani Fadila, Az Zahra Nur Fadli Mappisabbi Faizal Makhrus Farada Aulia Farika Nikmah Fariza Makmun Farraz Ariel Gubita Farraz Ariel Gumita Fauziah Rahmi Femmy Andrifianie Fenti Siregar Firmansyah Kholiq Pradana P.H. Fithria Sari Andalas Fitri Ernalis Fitri Hayati Fitria Marisya Ghanif Faoziansyah Ghina Reftantia Ghina Ulfa Saefurrohman gultom, martinus Hafiz Mursalan Hafsah Haikal Haliza, Siti Nur Halmadi Asmara Hamzah, Iswadi Harahap, Hasriyati Harahap, Indra Ramadona Hardiansyah Hasbih Sholeh Suryadi Hatidah Hatidah Helmi Helmy, Ahmad Hendra Harnanda Hendrias Hendrias Heni Susanti Herdianto Herlini Puspika Sari Herry Rahma Cahyadi Hibatullah Zuhdii Hidayat, Muhammad Ryan Husyairi, Khoirul Aziz Ibnu Utomo W.M. Ibrahim Ibrahim Ice Ratnalela Siregar Ida Adhani Ihsan Auliya Arrasyid Ihsanti Dwi Rahayu Ika Kurnia Sofiani Ika Lestari Salim ILHAM Imas Mulyani Immawati Asniar Indah Royani INDANA ZULFA Intan Sari Iradatul Hasanah Irma Irwan Syahputra, Irwan Isnaniah Isnawati, Iin Aini Ita Irianti Ita Iryanti Izzatul Ulya, Izzatul Izzatusolekha Jasiah Jasiah Jasiah Jeinvanny Tarigan Jesika Bakara Jordan Simanjuntak, Mikhael Kasmanto Rinaldi Khaeriyah Khaeriyah Khairul Khairul Amri Silalahi Khairul Khairul, Khairul Kiki Artika Krisnawati, Hani leddi Leni Marlina Leonard Tambunan Lestari Wuryanti Liber Sianipar Liber Tommy Hutabarat Lila Bismala Lucky Tirta Ardiansyah M Bambang Purwanto M Imam Santoso M Supriyadi M. Hanafiah M. Hayyun Alfarezi M. Rasyid M. Rifqi Akbar M.Dedek Hamdalah Putra M.Musa M.Musa M.Rizky Ramadhan Mahendra Widyartono Mahfudz Maissy Nabila Maisyura Maratus Solihah Mardhotillah Mardhotillah Mario Raka Narendra Marista Suryani Sitompul Marpaung, Dian Pratiwi Br. Marzuki Marzuki Mashudi Masrin Gafar Mia Puspita Michelle Jovelyna My Angel Pasaribu Miftahir Rizqa Miftahudin Nawawi Miftha Hulladuni Riandi Mitra Musika Lubis Mochammad Rivan Akhsa Moh. Ma'ruf Bantilan Moh. Slamet Sutrimo Mohammad Ahsanuddin Mohammad Sawir Moza Dela Fudika Muh Rezky Pratama Syamsuddin Muh Try Chaesar J Ongirwalu Muh. Ikhwan Fauzan Muhamad Sadikin Muhammad Akmal Muhammad Alfan Muhammad Fajar Muhammad Hafidz Azizi Muhammad Hasyim As’ary Muhammad Ibnu Sina Muhammad Khair Muhammad Maulana Muhammad Mutaqin Muhammad Muttaqin Muhammad Noor Muhammad Puja Dwi Surya Muhammad Rizal Muhammad Subianto Muhammad Syahrin Muhammad Zen, Muhammad Muntaromah Mustikawati Mutia Floresa Wea Mutiara Rengganis AR Nabila Karima Nadia Br Sembiring Nadira, Nadira Najmuddin Najmuddin Nata, Andri Natalia Michelle Simatupang Naufal Azriel Zheinuary Naufal Dzaky Arrachman Neti Regita Nila Rusiardi Jayanti Noniyanti, Noniyanti Nur Aisyah Nur Anisa Nur fath, Fath irsaz Nur Hamim Nurafni Yulianti Nurhayati Nurroyian Nursam Nursifa Nursifa Nursifa Nurul Haqqi Haqqoni Ismail Nyanyu Ully Auliana Oktoba, Zulpakor Omah Rochmah panjaitan, fahrul syahrijal Parhusip, Nelviony Popi Dea Ananda Pradana PH, Firmansyah Kholiq Pramesta, I Gede Ardi Putri Larasia Putri Nathasya Dhimastika Putri Suci Ramadhan R.A Rodia Fitri Indriani Rachmawati, Windasari Radhiah Zakaria Radillah, Teuku Radzu Anjani Rahardi Rahma Septiana Putri Rahmadani Rama Satya Tanjung Ramadhan Triyandi Randa Putra Kasea Sinaga Rasidin Karo-Karo Sitepu Ratna Putdyaningsih Redha Anshari Regita Cahyani Renni Meliana Sari Reza Maulana Dalimunthe Rezal, Muhammad Rian Farta Wijaya Ridhwan Rifki Awaludin Riki Habibi Rina Lestari Rini Setiawati Ririn Nurmandhani risma hayani Risma Indriana Rismahayani, Rismahayani Riza Afrian Mustaqim Riza Ul Fahmi Rizki Suwanda Rizqullah, Fikri Hassan Romadon, Taufik Rosyidi Hamzah Rosynanda Nur Fauziah Rozzi Kesuma Dinata Rudi Hariawan Rudi Hermawan Ruri Tri Astuti Safitri Handayani Safry Rahmatullah Saharuddin Daming Saiful Islam Abizar Salam, Rahmat Saleh, Lalu Muhammad Salsa Tiffani Rahmadona Salsabila Sofiya Wardani Salsabila Yusra Salwa Fadillah Sani Sari Wulandari Sarmila Satrio Rasendriya Widyadhana Satrio Rasendriya Widyahana Selsha Amalia Selvi Harviasantri Selvi Kasman Septania Indri Ariyani Setiasih Sherly Malini shilvi richiyanti Shilvirichiyanti Shilvirichiyanti Sipra Barutu Siregar, Julinda Siti Nurhayati SITI NURJANAH Sitorus, Zulham Soviana, Widya Sri Arlina Sri Aswaidar Miza SRI UTAMI Sri Wahyuni Suci Handayani Suci Julianti Nasution Sufi Laelawati Sugondo Hadiyoso Suhardiansyah Suhardiansyah SUHARSONO Sullaida Sulthani Mukhlis Sunarto S Sundari Surizki Febrianto Surtikanti Suryani Putri Susanto Susanto Susi Handayani Syahputri, Maulisa Syamsiar, Syamsiar Syarif Makmur Syariful Anami Tania Fadillah Tasril, Virdyra Taufik Ismail Al Adhim Taufik Jahidin, Taufik Taufik Sulaeman Puspanegara Teguh Rama Prasja Tria Patrianti Tsania Zahra Taslima Tuti Andriani Tutik Pebrianti Ummi Seibah A.S Vadiyani Fricillya Puteri Vilda Ana Veria Setyawati Vip Paramarta6 Vita Fibriyani Wa Ode Indang Wagiman Manik Wahyu Saputra Waode Suiyarti Widy Dwi Lestari Widyatmoko Karis Wiki Noviandi Wiwin Handoko Yati Rohayati Yeni Susanti Yessi Ratna Sari Yoyon Mulyana Darsuman Yudi Rahayu Yulia Margaretta Sari Yulia Rahmi Fitri Yulistia Devi Yulistua Devi Yuminah Rahmatulloh Yuni Oktopiyani Yusnita Ayu Hikma Yusrawati Yusrawati Yusrizal Yusrizal Yusuf Hendrawan Yuyun Wahyuni zainarti zainarti Zamhariri Zhulvardyan Armayrishtya Ziana Ziana Zidan Amukti Rajendra Zuhrinal M. Nawawi Zulaikah Zulfikar Zulham zulhelmi zulhelmi Zulqaidah

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Journal : Journal of Computer Science and Research