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All Journal Jurnal Ilmu Komputer Teknika Jurnal Informatika Sinergi Telematika Jurnal Informatika dan Teknik Elektro Terapan InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT Information Management For Educators And Professionals (IMBI) Informatika Pertanian Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Komtika (Komputasi dan Informatika) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Inotera Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Sains Riset Jurnal Sosial Humaniora Sigli JTIK (Jurnal Teknik Informatika Kaputama) JOURNAL OF INFORMATICS AND COMPUTER SCIENCE G-Tech : Jurnal Teknologi Terapan Jurnal Sistem Komputer dan Informatika (JSON) Medika Teknika : Jurnal Teknik Elektromedik Indonesia Idealis : Indonesia Journal Information System International Journal of Engineering, Science and Information Technology Conten : Computer and Network Technology Jurnal Teknik Informatika dan Elektro (JURTIE) TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Bangun Abdimas Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Jurnal Komtika (Komputasi dan Informatika) Jurnal Informatika Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Nurdin Nurdin
Universitas Malikussaleh
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Comparison of Naive Bayes and Dempster Shafer Methods in Expert System for Early Diagnosis of COVID-19 Nurdin Nurdin; Erni Susanti; Hafizh Al-Kautsar Aidilof; Dadang Priyanto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.2280

Abstract

COVID-19 is a respiratory infection disease caused by the corona virus. Transmission of this virus can spread very quickly so that the number of cases of the corona virus continues to grow and becomes an epidemic that spreads not only in Indonesia but also in other countries in the world. The purpose of this study is to build an expert system that is able to diagnose Covid-19 early by using a comparison of the Nave Bayes method and the Dempster Shafer method. The amount of data used in this study is 550 data, consisting of 500 training data and 50 testing data. While the variables used are symptoms related to COVID-19 as many as 17 symptoms consisting of G01, G02, G03, G04, G05, G06, G07, G08, G09, G10, G11, G12, G13, G14, G15, G16, G17. The diagnostic data consists of Suspected (PDP), Non-Suspected, and Close Contact (ODP). The results of the percentage test by comparing system diagnoses with expert diagnoses, for the nave Bayes method it has an accuracy of 96% with 48 diagnoses according to expert diagnoses from 50 tested data. Meanwhile, the Dempster Shafer method has an accuracy of 40% with 20 diagnoses according to expert diagnoses from 50 tested data. Based on the results of this study, the Naive Bayes and Dempster Shafer methods can be applied to an expert system for early diagnosis of COVID-19, from the results of the system testing the Naive Bayes method has better accuracy than the Dempster Shafer method.
Classification Analysis of Single Tuition Fees Using the Random Forest Method with K-Fold Cross Validation Khaidar, Al; Nurdin, Nurdin; Fajriana, Fajriana; Taufiq, Taufiq; Hamdhana, Defry
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11798

Abstract

Classification is the process of grouping data into specific categories based on their characteristics or features, which plays a crucial role in the analysis, decision-making, and prediction of new data. In academic settings, classification is used to determine the Single Tuition Fee to place students according to their economic ability. Lhokseumawe State Polytechnic has implemented the UKT system since 2020 with eight categories, but some students are still placed in UKT groups that do not match the results of the manual process, which has limited accuracy. This study uses the Random Forest method as a technology-based solution to improve the accuracy and objectivity of UKT classification. The dataset used consists of 10,000 student data with 10 variables, covering economic and social information. The research process includes data preprocessing, Random Forest model training, performance evaluation using accuracy, precision, recall, and F1-score, and model stability testing through 10-fold K-Fold Cross Validation. The results show that Random Forest is able to classify most UKT classes well, especially classes 0–5 and 7. Class 6 has lower performance with a recall of 0.39 and an F1-score of 0.56 due to the limited number of samples. The overall accuracy of the model reaches 96%, while K-Fold Cross Validation produces an average accuracy of 95.50% with a standard deviation of 0.66%, indicating the model is stable and able to generalize to new data. This study proves that Random Forest is effective in UKT classification, producing an objective, fair, and efficient system. This implementation model supports data-driven decision-making in higher education and increases transparency in UKT determination.
Analisis Perbandingan Kinerja Algoritma You Only Look Once (YOLOv8) Dan Single Shot Detector (SSD) dalam Pengenalan Nominal Uang Kertas Ulfah, Julia; Ula, Munirul; Fajriana, Fajriana; Nurdin, Nurdin
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.7471

Abstract

The advancement of technology in the field of image recognition has significantly facilitated and improved the effectiveness of object detection in computer-based banknote recognition systems. This study aims to automatically identify banknotes based on their denominations, with the objective of minimizing human errors—such as lack of concentration, fatigue, and other factors—and enabling its application in ATMs and automated payment systems. This research compares the accuracy levels and detection success rates between the YOLO and SSD algorithms in recognizing the denominations of banknotes. The YOLO model operates by dividing the image into grids and predicting bounding boxes along with object classes in a single step, resulting in fast and consistent detection. In contrast, the SSD model employs a multi-scale approach by utilizing feature maps from multiple levels to generate predictions. The parameters used in this study include 7 classes of Indonesian banknotes: Rp1,000, Rp2,000, Rp5,000, Rp10,000, Rp20,000, Rp50,000, and Rp100,000. A total of 353 images were used in the dataset, and three images from each class were selected for testing purposes. The results of the study indicate a significant performance difference. The YOLO algorithm achieved a 100% accuracy rate under both normal and low-light conditions, while the SSD algorithm achieved an accuracy rate of 87.2% under normal lighting and 91.4% under low-light conditions.
Implementation of Triple Exponential Smoothing in Predicting Blood Stock Inventory Afif Diapari Ma'aruf Lubis Afif Diapari; Nurdin; Kurniawati
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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

Abstract

Blood availability is an important component for the Indonesian Red Cross (PMI) Blood Donor Unit (UDD) in maintaining blood supplies so that blood is not wasted and there is no shortage. This study aims to test the effectiveness of using the Triple Exponential Smoothing (TES) method in predicting blood stock inventory at UDD PMI. Triple Exponential Smoothing is a forecasting method that considers seasonal patterns in data, which is relevant in predicting blood demand based on historical data. This study began by collecting historical blood stock data from January 2019 to December 2023. Next, the data was analyzed to identify seasonal patterns and trends. This method is applied to the four main blood types (A, B, AB, and O) by calculating the accuracy value using Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The results show that the TES method can accurately predict blood availability and demand, with a low MAPE value of 2.15% for blood type A. For blood type B, the MAPE value is 1.38%, blood type O is 1.03%, and blood type AB is 2.42%. This research is expected to significantly contribute to more effective and efficient bloodstock management at PMI and become an academic reference for future blood stock forecasting studies.
Analisis Efektivitas Metode Naïve Bayes terhadap Sentimen Opini Publik di X Mengenai Relokasi Ibu Kota Indonesia (IKN) Anas, Mukhtar; Nurdin, Nurdin; Taufiq, Taufiq
Jurnal Teknik Informatika dan Elektro Vol 8 No 1 (2026): Jurnal Teknik Elektro dan Informatika
Publisher : Universitas Gajah Putih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55542/jurtie.v8i1.1874

Abstract

Selesainya Rancangan Undang-Undang (RUU) pemindahan ibu kota pada awal September 2021, pembangunan Ibu Kota Nusantara (IKN) resmi dimulai pada bulan Juli tahun 2022. Persoalan ini mengundang pro dan kontra di masyarakat, karena kepentingan dan pandangan masing-masing. perkembangan dunia digital semakin canggih, informasi lebih mudah didapatkan melalui portal dan media sosial. Media sosial merupakan salah satu tempat untuk menyampaikan opini masyarakat. Melalui text mining di X kita dapat memahami gambaran orang dalam persepsi mereka terhadap kebijakan pemerintah, baik Positif, Netral dan Negatif. Analisis sentimen ini perlu dilakukan, oleh karena itu penelitian Analisis Efektivitas Metode Naïve Bayes Pada Sentimen Opini Publik Di X Terhadap Pemindahan Ibu Kota Nusantara (IKN) dilakukan untuk melihat polarisasi opini masyarakat dan menguji efektivitas metode Naïve Bayes terhadap dataset IKN. Rancangan penelitian cross sectional karena data yang diambil dari aplikasi X sejak tanggal 18 Januari 2022 sampai dengan 3 Maret 2024. Hasil screping data didapatkan sebanyak 3775 tweet, kemudian dicleaning sehingga menghasilkan 2778 data tweet. Dalam scrapping dan analisis data tweet alat yang digunakan google colab dan bahasa pemograman phyton. Data dibagi menjadi data latih sebanyak 80% dan data uji 20%. Polarisasi sentimen tweet positif sebanyak 1153 (39.32%) Netral 991 (33.80%) dan Negatif 788 (26.88%). Tingkat akurasi untuk tiap tiap jenis naïve bayes, GaussianNB 0.5168, MultinomialNB 0.6133 dan bernoulliNB 0.6115. hasil uji akurasi menggunakan confusion matrix menggambarkan metode Naïve Bayes dengan jenis MultinomialNB mendapatkan akurasi yang lebih tinggi dalam mengklasifikasi dataset IKN
Application of Market Basket Analysis with the Apriori Algorithm to Discover Consumer Behavior Patterns Through Transaction Data S.Kom., M.Kom (SCOPUS ID=ID: 57201646662), Nurdin; Abdurraafi, Muthrib; Ar-Razi, Ar-Razi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.3905

Abstract

Market Basket Analysis (MBA) examines itemsets that are purchased together by customers in a single transaction and is commonly used to analyze consumer behavior patterns based on transaction data. Kaffah Mart is a supermarket that sells daily necessities and household products. However, the store has not yet identified consumer shopping patterns within customers’ shopping baskets. This study aims to identify product association patterns formed through the application of Market Basket Analysis and to determine appropriate marketing strategies based on the generated association rules using the Apriori algorithm. The findings of this research are expected to support the development of more effective marketing strategies, thereby increasing product sales profitability at Kaffah Mart. The research methodology consists of the following stages: data collection, system flowchart design, implementation of the Apriori algorithm, and system deployment. The results show that, for the 3-itemset rules, customers who purchase sweet soy sauce and chili sauce are also likely to purchase instant noodles. Similarly, customers who buy a toothbrush and mouthwash are also likely to purchase toothpaste, with a confidence value of 100%. For the 2-itemset rule, customers who purchase shampoo are also likely to purchase bath soap, with a confidence value of 96.87%.
Analysis of the Efficiency and Performance Effectiveness of Srikandi Application Using the UTAUT Model and Delone & Mclean Syahputra, Wawan; Abdullah, Dahlan; Nurdin, Nurdin; Daud, Muhammad; Taufiq, Taufiq
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1806

Abstract

The development of information technology has encouraged the government to carry out digital transformation in administrative governance, one of which is through the implementation of the SRIKANDI Application (Integrated Dynamic Archive Information System). This application is designed to support the management of electronic archives and correspondence integrated across government agencies. This study aims to analyze the efficiency and effectiveness of the SRIKANDI Application in supporting government administration, focusing on service speed, documentation accuracy, and resource efficiency. The method used in this study is a mixed methods approach with a sequential explanatory design. Quantitative data were collected by distributing questionnaires to employees who used the application to assess perceptions of efficiency and effectiveness. Furthermore, qualitative data were obtained through in-depth interviews and document analysis to delve into the quantitative findings and explore contextual factors that influence application implementation. Data analysis is carried out in stages, starting with descriptive and inferential statistical analyses for quantitative data and with thematic analysis for qualitative data. This research is expected to contribute to the development of an electronic government system and serve as a reference for evaluation and policymaking related to bureaucratic digitalization. In addition, the results of this study are also expected to strengthen the literature on the effectiveness of government information systems and provide an empirical picture of the practice of implementing the SRIKANDI Application in government agencies.
Comparison of Logistic Regression and Random Forest Methods in Predicting Vehicle Tax Payment Compliance Khairul Fuadi; Taufiq Taufiq; Arnawan Hasibuan; Dahlan Abdullah; Nurdin Nurdin
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v13i1.11944

Abstract

Motor vehicle tax is a major source of Regional Original Income (PAD). However, the level of motor vehicle tax payment compliance in North Aceh Regency is still suboptimal, particularly related to late payments. A data-driven approach is needed to predict and understand taxpayer compliance patterns more accurately. This study aims to compare the performance of the Logistic Regression and Random Forest methods in predicting motor vehicle tax payment compliance, as well as to identify factors that influence taxpayer compliance behavior at the North Aceh Samsat (Sat). This study uses secondary data in the form of motor vehicle tax payment transactions at the North Aceh Samsat for the 2022–2024 period, totaling 100,000 observations. The response variable is the tax payment compliance status (compliant and non-compliant), while the predictor variables include vehicle age, type of ownership, vehicle type, and vehicle brand. The data is divided into 70% training data and 30% testing data. The performance evaluation model is conducted using accuracy, precision, recall, and Area Under Curve (AUC) metrics. The analysis results show that Random Forest has better predictive performance than Logistic Regression, with higher accuracy and AUC values. Vehicle age and type of ownership are the most influential variables in predicting tax payment compliance, while vehicle brand has a relatively smaller influence. Logistic Regression provides a clear interpretation of the variable relationship, but has lower discrimination ability than Random Forest. Random Forest has proven to be more effective as a prediction model for motor vehicle tax payment compliance at the North Aceh Samsat. The application of machine learning-based predictive models has the potential to support more targeted policy making in an effort to improve motor vehicle tax payment compliance, especially in reducing late payments.
Co-Authors - Miranda ., Muthmainah Abdurraafi, Muthrib Adi Prasetyo Afif Diapari Ma'aruf Lubis Afif Diapari Afrilia, Yesy Aidilof, Hafizh Al Kautsar Al Khaidar Alaiya, Azna Alqhifari, Azka Ama Zanati Amalia, Nova Amin Munthoha Aminsyah, Ansharulhaq Ananda Faridhatul Ulva Anas, Mukhtar Andri Alfitra Anggara, Aji Ar-Razi, Ar-Razi Arnawan Hasibuan Aynun, Aynun Aynun, Nur Azzanna, Maghriza bhakti wan khaledy Bustami Bustami Bustami Bustami Cesilia, Yolinda Chaeroen Niesa Chicha Rizka Gunawan Cut Agusniar Dadang Priyanto Dahlan Abdullah Darmansyah, Arif Desky, Muhammad Aulia Dewi Astika Erni Susanti Eva Darnila Fadlisyah Fadlisyah Fadlisyah Fahrozi, Fazar Fajriana Fajriana Fajriana, Fajriana Fasdarsyah Fasdarsyah fatimah Fatimah Fikhri, Aditya Aziz Fikran, Rifzan Fikri Fikri Gavinda, Virza Ginting, Andriyan gunawan, chicha rizka Gunawan, Chichi Rizka Hafizh Al Kautsar Aidilof Hafizh Al-Kautsar Aidilof Hamdhana, Defry Herman Fithra Hermansyah Hermansyah I Made Ari Nrartha Ilyana, Anis Imanda, Nanda Intan Nuriani Isa, Muzamir Ismun Naufal Jessika, Jessika Jikti Khairina Julia Ulfah Khaidar, Al Khairina, Jikti Khairul Fuadi Khairul Khairul, Khairul Khairuni Khairuni Kurnia, Sri Kurniawati M Farhan Aulia Barus M Rizwan M Suhendri M. Ali, Rahmadi Marleni Marleni Maryana Maryana Maryana Maryana Maryana Maryana Maryana, Maryana Maulita, Maya Maya Juwita Dewi Maysura Meriatna Meriatna Muchlis Abdul Muthalib Muhammad Daud Muhammad Daud Muhammad Faisal Muhammad fauzan Muhammad Fikry Muhammad Furqan, Muhammad Muhammad Hutomi Muhammad Iqbal Muhammad Johan Setiawan Muhammad Nasir Muhammad Riansyah Muhammad Ridha Mukti Qamal Muliana, Syarifah Munirul Ula Mutammimul Ula Muzakir Nur Nadilla Baimal Puteri NELI SUSANTI, NELI Nunsina, Nunsina Nur, Muzakir Pradita, Cindy Cika Rahmad Rahmad Rahmad Rahmat Rahmat Raihan Putri Rasyada, Reza Dian Reza, Restu Rini Meiyanti Risawandi, Risawandi Riza Mirza Rizal S.Si., M.IT, Rizal Rizki Setiawan Rizki Suwanda Rizky Putra Fhonna Rizkya, Ghinni Robi Kurniawan Rusadi, Athirah salamah salamah Salimuddin, Salimuddin Salsabila, Thifal Samudera, Brucel Duta Sapitri, Anggri Sari, Cut Jora Sayuti, Muhammad Siagian, Tania Annisa Siregar, Widyana Verawaty Sri Kurnia Suci Fitriani, Suci Suhaili Sahibul Muna Sujacka Retno Sultan, Kana Suryana, Fitra Syahputra, Wawan Syandriani Harahap Taufik Taufik Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Uci Mutiara Putri Nasution Ulfah, Julia Ulva Fitriani Wahdana, Aldi Wan, Syahputra Wawan Wawan Yani, Muhamamd Yeni Yeni Yesy Afrilia Yesy Afrillia Yulisda, Desvina Zahrah, Violita Aditya Zahratul Fitri Zahratul Fitri, Zahratul Zalfie Ardian Zara Yunizar Zuraida Zuraida