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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Sains Riset JTIK (Jurnal Teknik Informatika Kaputama) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Pengabdian kepada Masyarakat Nusantara International Journal of Engineering, Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Media Informatika Jurnal Info Sains : Informatika dan Sains International Journal of Electronics and Communications Systems Jurnal Ilmiah Multidisiplin Indonesia Multidisiplin Pengabdian Kepada Masyarakat (M-PKM) Jurnal Sistem Informasi dan Teknologi Jaringan
Nababan, Adli Abdillah
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SMART APPLICATION FOR AUTISM DIAGNOSIS IN TODDLERS USING THE NAIVE BAYES METHOD IN LANGKAT REGENCY Ryan Dhika Priyatna; Nababan, Arif Hamied; Nababan, Adli Abdillah; Miftahul Jannah; Harry Pratama Figna
INFOKUM Vol. 10 No. 5 (2022): December, Computer and Communication
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v10i5.1199

Abstract

All parents expect to have healthy, proud, and perfect children, however, sometimes things don't go the way they want. Some parents get the child they want and some don't. Some of them have children with special needs, such as autism. In Indonesia, each year, children with autism continue to increase. In 2015, it is estimated that there are approximately 12,800 children with autism and 134,000 children with the Autism spectrum in Indonesia. In addition to the lack of information and knowledge, preconceived notions also make parents reluctant to hand over their children for treatment. This research is motivated by the lack of solutions offered to existing problems by utilizing the development of information technology. The rapid development of technology in Indonesia has contributed a lot to the problems experienced by society with the birth of various kinds of smart systems. The development of a smart system based on an expert system can be a solution to the diagnosis of autism that appears in toddlers. The expert system that is offered is a system that can diagnose autism in toddlers in the Langkat district by implementing a method that can make decisions by providing the best solution. A good method is a method that has a high level of accuracy. The method used in this study is the Naive Bayes method where this method has been proven to be able to solve complex problems by predicting existing probabilities.
Application of naive bayes algorithm for dominant disease classification in coastal environments Nababan, Adli Abdillah
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 01 (2024): Informatika dan Sains , Edition March 2024
Publisher : SEAN Institute

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

Abstract

This research focuses on the implementation of the Naive Bayes algorithm to classify prevalent diseases in coastal areas. Coastal regions, characterized by unique environmental factors and limited healthcare accessibility, pose distinct challenges to public health. The primary objective of this study is to enhance the precision and understanding of disease diagnosis within these regions. By employing data analysis and machine learning techniques, the research aims to contribute significantly to the prevention, management, and treatment of diseases in coastal areas, ultimately improving the well-being of local communities. Additionally, the findings have the potential to assist governments and health institutions in formulating targeted and efficient health policies for coastal areas. A comprehensive understanding of dominant disease patterns enables data-driven decision-making, influencing the allocation of health resources, distribution of vaccines and medicines, and the design of tailored prevention programs. Overall, this research is poised to yield substantial benefits by advancing healthcare and enhancing the quality of life in coastal communities.
IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR DALAM KLASIFIKASI CITRA SAYUR Nababan, Adli Abdillah; Jannah, Miftahul
Jurnal Sains Riset Vol 13, No 2 (2023): September 2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) Universitas Jabal Ghafur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47647/jsr.v13i2.1754

Abstract

Klasifikasi berdasarkan warna sayuran tidak selalu menjadi satu-satunya faktor yang digunakan untuk menilai jenis sayuran, Tapi hal tersebut bisa menjadi petunjuk pertama untuk menilai jenis sayuran secara visual sebelum memutuskan untuk menjual atau mengkonsumsinya. Petani dapat menggunakan teknologi ini untuk memantau dan mengontrol kualitas vegetasi dengan lebih baik menggunakan teknik pemilahan yang dirancang untuk mencapai hasil optimal dan kualitas vegetasi yang lebih baik. Kajian ini cukup penting untuk memberikan informasi kepada masyarakat tentang penilaian sayuran berdasarkan warna. Dengan sistem yang dibangun, Desa Pagar Merbau II dapat dimajukan terutama dalam pemanfaatan hasil tanaman sayuran para petani. Sistem penilaian warna sayuran dapat digunakan untuk mengedukasi masyarakat untuk meningkatkan hasil sayuran dan menciptakan kesadaran umum untuk mengkonsumsi sayuran segar berdasarkan warna. pada Penelitian ini, teknik Klasifikasi kualitas sayur berdasarkan warna menggunakan pendekatan pengolahan citra digital untuk menentukan jenis sayur menggunakan metode K-Nearest Neighbor untuk menentukan jenis sayur. pada penelitian ini jenis sayur yang digunakan adalah Bayam, Kol, Wortel dan Terong. Adapun hasil klasifikasi pada jenis sayuran berdasarkan warna terhadap 40 sampel data pengujian gambar menggunakan metode KNN sebesar 87,5%.
Penentuan Nilai Harga Jaminan Barang Elekronik Menggunakan Metode Weight Product Di PT. Indonesia Gadai Oke Martiano, Martiano; Nababan, Adli Abdillah; Luky Harefa, Ade May; Marnoko, Marnoko; Adhitya Pratama, Yudhistira; Dharshinni, N. Priya
Jurnal Media Informatika Vol. 6 No. 2 (2025): Jurnal Media Informatika
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i2.5371

Abstract

PT. Indonesia Gadai Oke membutuhkan sistem pendukung keputusan (SPK) yang efektif dan efisien untuk menentukan kreditur terbaik. Metode Weight Product (WP) dipilih karena kemampuannya menyelesaikan masalah keputusan multi-kriteria dengan memberikan bobot pada kriteria yang relevan, yaitu merek, jenis, tipe, dan kondisi barang. Proses penilaian menggunakan metode WP melibatkan penghitungan nilai vektor untuk setiap alternatif berdasarkan bobot kriteria, yang kemudian diurutkan dan dibandingkan dengan hasil penentuan harga yang dilakukan oleh perusahaan. Hasil penelitian menunjukkan bahwa metode WP mampu memberikan hasil yang akurat dengan tingkat keberhasilan mencapai 83%. Akurasi ini membuktikan bahwa metode WP sesuai dengan kebutuhan perusahaan dalam menentukan harga barang elektronik secara objektif dan konsisten. Selain meningkatkan efisiensi, metode ini juga mendukung transparansi proses pengambilan keputusan, sehingga dapat meningkatkan kepercayaan konsumen terhadap layanan perusahaan. Dengan implementasi metode WP, PT. Indonesia Gadai Oke dapat mengoptimalkan proses penilaian harga barang jaminan elektronik. Ke depan, perusahaan dapat mempertimbangkan pengembangan sistem dengan menambahkan kriteria lain, seperti tingkat risiko atau hubungan kreditur dengan pihak bank, untuk meningkatkan komprehensivitas hasil. Metode WP memberikan fondasi yang kuat untuk membangun SPK yang lebih maju dan adaptif.
Pengembangan Sistem Informasi Tugas Akhir Berbasis Website Adhitya Pratama, Yudhistira; Nababan, Adli Abdillah; Ade Maulana; Purwa Hasan Putra; Nababan, Arif Hamied
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 5 No 1 (2024): Maret
Publisher : CV. ADMITECH SOLUTIONS

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

Abstract

The rapid development of information technology has brought many changes in the field of education, one of which is the use of web-based information systems to support the process of completing student's final projects. The final project is an essential component in higher education programs, but often faces various challenges, such as coordination between students and supervisors, document management, and scheduling of activities. To address these issues, this research aims to develop a web-based final project information system that can help students and supervisors manage and monitor the final project completion process effectively. This research uses the Waterfall system development method, which consists of the stages of requirement analysis, system design, implementation, testing, and maintenance. In the requirement analysis stage, the researchers identify and document the system requirements, including the main features, functionality flow, user interface, and constraints that must be met. Intensive and collaborative communication with users, namely supervisors and students, is crucial at this stage. By understanding the system requirements comprehensively, the researchers can design appropriate solutions that meet user expectations.
Pengembangan Sistem Informasi Pengelolaan Jadwal dan Ruangan berbasis Website Adhitya Pratama, Yudhistira; Pratama, Yudhistira Adhitya; Nababan, Adli Abdillah; Maulana, Ade; Dulianto, Des; Harefa, Ade May Luky
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 5 No 2 (2024): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v5i2.42

Abstract

The rapid development of information technology has significantly impacted various aspects of life, including management and administration. Efficient scheduling and room management remain a major challenge for educational, governmental, and private institutions. Manual processes are often time-consuming, prone to errors, and lack flexibility in responding to dynamic changes. This research aims to design and develop a web-based information system for scheduling and room management to address these challenges effectively. The system provides features such as building and room management, schedule management, and user account handling, enhancing accessibility and reducing overlapping schedules and allocation errors. The development process involves system requirement analysis and modeling using Use Case and Entity Relationship Diagrams. The resulting system simplifies real-time monitoring, automates manual processes, and improves institutional operational efficiency. Testing through black-box methods confirmed the system's functionality and user-friendliness, ensuring reliable implementation for users. This study contributes to technological advancement by offering a practical solution to operational inefficiencies while laying the groundwork for further enhancements in system functionality and user interface design.
DIGITALISASI PROSES PENJUALAN MELALUI WEB BASED POINT OF SALE PADA WARUNG DEK GAM KUPHI Nababan, Adli Abdillah; Hasugian, Paska Marto; Miftahul Jannah; Harefa, Ade May Luky
Multidisiplin Pengabdian Kepada Masyarakat Vol. 4 No. 01 (2025): Multidisiplin Pengabdian Kepada Masyarakat, Maret-Juni 2025
Publisher : Sean Institute

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Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan efisiensi operasional dan akurasi pencatatan transaksi penjualan pada Warung Dek Gam Kuphi melalui penerapan sistem Point of Sale (POS) berbasis web. Warung tradisional pada umumnya masih melakukan pencatatan secara manual sehingga rentan terhadap kesalahan, kehilangan data, serta tidak mampu menyajikan laporan penjualan yang cepat dan akurat. Dalam kegiatan ini, tim pelaksana merancang dan mengimplementasikan sistem POS berbasis web yang dapat diakses melalui perangkat komputer maupun smartphone. Proses pelatihan dan pendampingan kepada mitra juga dilakukan secara langsung agar mitra memahami cara penggunaan sistem secara menyeluruh. Hasil kegiatan menunjukkan bahwa mitra dapat dengan mudah mengoperasikan sistem tersebut dan merasa terbantu dalam pencatatan penjualan, pengelolaan stok barang, serta pelaporan keuangan harian. Kegiatan ini memberikan dampak positif dalam mendorong digitalisasi usaha mikro, khususnya di sektor perdagangan kecil. Kedepan, sistem ini dapat dikembangkan lebih lanjut dengan integrasi metode pembayaran digital dan fitur analisis penjualan untuk mendukung pengambilan keputusan bisnis yang lebih baik.
Comparison of Xgboost, Random Forest and Logistic Regression Algorithms in Stroke Disease Classification Sitompul, Lia Relita; Nababan, Adli Abdillah; Manihuruk, Mey Lestari; Ponsen, Wildan Andika; Supriyandi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14794

Abstract

Stroke remains a critical global health concern, ranking as the second leading cause of mortality and third cause of disability worldwide. Early detection and accurate classification of stroke risk could significantly improve patient outcomes through timely interventions. This research evaluates and compares the performance of three machine learning algorithms—XGBoost, Random Forest, and Logistic Regression—for stroke disease classification using a dataset of 5,110 patient records with 12 attributes including demographic, lifestyle, and health factors. Due to significant data imbalance between stroke and non-stroke cases, Synthetic Minority Over-sampling Technique (SMOTE) was applied to enhance model performance. Comprehensive evaluation metrics including accuracy, precision, recall, and F1-score were utilized to assess each algorithm's effectiveness. Results demonstrate that XGBoost achieved superior performance with 95% accuracy, followed by Random Forest at 94% and Logistic Regression at 82%. Feature importance analysis identified age, average blood glucose level, and history of heart disease as the most significant predictors for stroke diagnosis. This study contributes to the advancement of clinical decision support systems by highlighting the effectiveness of ensemble learning approaches for stroke prediction, potentially enabling earlier interventions and improved patient management. These findings suggest that integration of machine learning tools in clinical settings could enhance stroke risk assessment, though further validation with diverse patient populations is recommended for broader implementation.
TaniMarket: An E-Commerce Platform for Empowering Local Agricultural MSMEs Miftahul Jannah; Nababan, Adli Abdillah; Medhian Ahmadi Putra; Jijon Raphita Sagala; Harefa, Ade May Luky
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 4 No. 05 (2025): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), June 2025
Publisher : Sean Institute

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Abstract

The development of information technology provides a great opportunity for Micro, Small, and Medium Enterprises (MSMEs) to market their products digitally, including in the agricultural sector. However, there are still many agricultural MSME actors who have not utilized this technology optimally. This research aims to build a webbased information system called TaniMarket, which can be used as a means of marketing agricultural products by MSME actors. Application development is carried out using the Waterfall method, which includes the stages of needs analysis, system design, implementation, testing, and maintenance. The application is built using PHP programming language version 8.1, the CodeIgniter 4 framework, and the MySQL database. This system consists of two main roles, namely admin and user. Admins have full access to product and category management features, including adding, modifying, and deleting data, as well as uploading images and order information. Meanwhile, users can view a list of products by category and place orders directly through the WhatsApp button that has been provided with automatic messages. The results of the system test show that all features run according to the design that has been determined. The TaniMarket application is considered to be able to provide convenience for MSME actors in marketing their garden products more widely and efficiently. With a simple and responsive interface, this system also supports users in accessing product information quickly and practically. Overall, this application is an effective digital solution in supporting the increase in the competitiveness of agricultural MSMEs in the era of digital transformation.
Smart Campus Dropout Prediction: Hybrid Features and Ensemble Approach Safii, M; Nababan, Adli Abdillah; Husain, Husain
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1183

Abstract

The issue of the high number of students dropping out of college is a major concern in higher education, especially in the smart campus ecosystem. This research aims to design a prediction system for students who are at risk of dropping out by integrating hybrid feature selection methods and ensemble learning that leverage academic data and students' digital footprints. The initial process of model development involves data cleaning and the selection of important features through a combination approach using filter-based methods (mutual information) and recursive feature elimination. A classification model is then designed using the XGBoost and Random Forest algorithms. The testing was conducted using a secondary dataset that included variables such as participation in discussions, attendance rates, interaction with learning materials, and academic achievement. The results of testing with the XGBoost model showed a satisfactory accuracy level, with an F1 score of 0.77 and a ROC AUC of 0.89. The confusion matrix recorded 67 correct predictions for students who graduated and 17 correct predictions for students who dropped out, with a total of 12 misclassifications. These findings suggest that the combination of hybrid feature selection strategies and XGBoost can produce sufficiently accurate predictions of student dropouts and has the potential to be utilized as an early warning system in the governance of a more flexible and responsive smart campus.
Co-Authors Ade Maulana Adhitya Pratama, Yudhistira Dulianto, Des Harefa, Ade May Luky Harry Pratama Figna Hasugian , Paska Marto Husain Husain Jijon Raphita Sagala Luky Harefa, Ade May Manihuruk, Mey Lestari Marnoko, Marnoko Martiano, Martiano Medhian Ahmadi Putra MIFTAHUL JANNAH Miftahul Jannah Muhammad Azhan Maulana N P Dharshinni Nababan, Arif Hamied Ningsi, Yuwanda Sri Ponsen, Wildan Andika Putra, Purwa Hasan Riksa Nadia Ryan Dhika Priyatna Safii, M Sitompul, Lia Relita Supriyandi Yudhistira Adhitya Pratama Yulia, Dita