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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 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)
Nurdin Nurdin
Universitas Malikussaleh
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Implementation Of Static Routing And Quality Of Service For Optimization Of Network Traffic Management On Cisco Routers Hermansyah, Hermansyah; Khaidar, Al; Nurdin, Nurdin; Kurnia, Sri
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7381

Abstract

Di era digital, kebutuhan akan jaringan yang andal dan efisien menjadi krusial untuk mendukung pertukaran data yang lancar. Lalu lintas data yang padat dapat menurunkan kualitas layanan, menyebabkan keterlambatan transmisi, dan meningkatkan risiko kehilangan paket. Penelitian ini mengimplementasikan metode static routing dan Quality of Service (QoS) sebagai strategi manajemen lalu lintas jaringan untuk meningkatkan efisiensi dan stabilitas komunikasi pada router Cisco. Metode yang digunakan meliputi konfigurasi static routing untuk mengatur jalur data secara manual dan penerapan QoS untuk memprioritaskan jenis layanan berdasarkan parameter latency dan packet loss. Hasil pengujian melalui simulasi dua router Cisco menunjukkan konektivitas yang stabil, dengan waktu respons rendah dan tanpa kehilangan paket signifikan. Nilai latency tercatat di bawah 150 ms dan packet loss kurang dari 1%, memenuhi kategori “Sangat Bagus” menurut standar TIPHON. Kombinasi static routing dan QoS terbukti efektif dalam mengoptimalkan manajemen lalu lintas jaringan.
Sentiment Analysis Of Instagram Comments On The BPS Province X Account Using The Naive Bayes Algorithm Based On Machine Learning Jessika, Jessika; Khaidar, Al; Nurdin, Nurdin; Muliana, Syarifah
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7815

Abstract

Sentiment analysis is an approach in natural language processing that aims to identify and categorize user opinions or attitudes towards an entity based on text data. The data used consists of the last 500 uploaded captions obtained through the Phantombuster tool. The analysis stages include data crawling, preprocessing (removal of duplicate and empty data, tokenization, stopword removal, and case folding), printing using the Naïve Bayes algorithm, and visualization of the classification results. Based on the processing results, it was found that the majority of the data was classified as neutral (97.65%), while the rest was divided into positive (1.57%) and negative (0.78%) categories, with a model accuracy of 94%. Although the model accuracy is relatively high, the dominance of the neutral class indicates an imbalance in data distribution (imbalanced data) which can affect the quality of the generalization model.
Analysis Of Customer Understanding Level Of The E-Policy System In The Sinar Mas Online Insurance Application In The Lhokseumawe Branch Work Area Muliana, Syarifah; Nurdin, Nurdin; Alqhifari, Azka; Khaidar, Al; Jessika, Jessika
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7824

Abstract

Digital transformation in the insurance industry is driving companies to adopt electronic systems, including the implementation of e-policies as a replacement for physical policy documents. This study aims to analyze the level of customer understanding of the e-policy system on the Sinar Mas Online Insurance application in Lhokseumawe branch. The research method used is a quantitative approach with data collection techniques through distributing questionnaires to 100 active customers. The results show that most customers are aware of the existence of e-policies, but still face obstacles in understanding their functions, legality, and how to access documents through the Sinar Mas Online application. Factors such as age, education level, and experience using digital services have been shown to influence the level of customer understanding. This study recommends the need for continuous education and the development of a more intuitive application interface to improve digital literacy and user convenience in accessing e-policies. These findings are expected to provide evaluation material for companies in improving their information systems and digital communication strategies for customers.
Comparative Analysis of Random Forest Algorithms, Artificial Neural Networks, and Logistic Regression in Breast Cancer Prediction with Machine Learning Approach M. Ali, Rahmadi; Nurdin, Nurdin; Khaidar, Al; Azzanna, Maghriza; Rusadi, Athirah
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7028

Abstract

Perkembangan teknologi informasi khususnya kecerdasan buatan dan machine learning, telah meningkatkan efektivitas deteksi dini penyakit seperti kanker payudara. Namun, tingginya angka kejadian dan kematian akibat kanker payudara di Indonesia masih menjadi tantangan besar, terutama karena rendahnya tingkat deteksi dini dan banyak pasien datang dalam stadium lanjut. Penelitian ini membandingkan performa tiga algoritma machine learning, yaitu Random Forest, Artificial Neural Network (ANN), dan Logistic Regression, dalam memprediksi diagnosis kanker payudara berdasarkan akurasi, efisiensi komputasi, dan kestabilan kinerja. Evaluasi dilakukan dengan classification report dan validasi silang 10-Fold Cross Validation. Hasil menunjukkan Logistic Regression memiliki akurasi rata-rata tertinggi sebesar 77,56% dan waktu eksekusi tercepat, yaitu 0,024897 detik, menandakan efisiensi dan kestabilan yang baik. Random Forest memberikan akurasi classification report 80% dan nilai AUC tertinggi 0,89, menunjukkan keunggulan dalam diskriminasi kelas. ANN memiliki performa terendah dengan akurasi validasi silang 74,64% dan recall rendah untuk kelas positif. Logistic Regression direkomendasikan sebagai model paling seimbang, sementara Random Forest sebagai alternatif akurasi tinggi.Kata Kunci: Random Forest, Artificial Neural Networks, Logistic Regression, Breast Cancer Prediction, Machine Learning
Penerapan Model Program Belajar Bekerja Terpadu untuk Menciptakan Lapangan Kerja Nurdin, Nurdin; Taufiq, Taufiq; Maryana, Maryana; Fadlisyah, Fadlisyah
Jurnal Malikussaleh Mengabdi Vol. 4 No. 2 (2025): Jurnal Malikussaleh Mengabdi, Oktober 2025
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v4i02.24825

Abstract

Transformasi digital di era ekonomi modern menuntut integrasi yang lebih erat antara dunia pendidikan tinggi dan dunia usaha, khususnya UMKM sebagai tulang punggung perekonomian nasional. Di sisi lain, lulusan program studi sering menghadapi tantangan dalam memperoleh pengalaman kerja nyata dan peluang kerja yang sesuai kompetensi. Untuk menjawab tantangan tersebut, diperlukan suatu pendekatan kolaboratif yang mampu menjembatani kebutuhan dunia industri, terutama UMKM, dengan potensi dan kompetensi alumni perguruan tinggi. Program pengabdian ini bertujuan untuk menerapkan model Belajar Bekerja Terpadu (Work Integrated Learning) yang melibatkan alumni program studi Teknik Informatika dari desa lingkungan Universitas Malikussaleh dalam upaya menciptakan lapangan kerja sekaligus mendukung pengembangan Usaha Mikro, Kecil, dan Menengah (UMKM). Melalui kolaborasi ini, alumni tidak hanya memperoleh pengalaman kerja riil dan peningkatan kompetensi, tetapi juga berperan aktif dalam digitalisasi dan peningkatan produktivitas UMKM. Model ini dirancang untuk menjembatani kesenjangan antara dunia pendidikan dan kebutuhan industri kecil, dengan pendekatan yang berfokus pada pemecahan masalah riil di lapangan. Hasil yang diharapkan dari kegiatan ini meliputi peningkatan kemampuan kerja lulusan, terciptanya peluang usaha baru, serta transformasi digital pada UMKM mitra. Program ini sekaligus menjadi strategi konkret dalam memperkuat peran perguruan tinggi dalam pembangunan ekonomi berbasis masyarakat
Sentiment Analysis of E-Commerce Product Reviews on Tokopedia Using Support Vector Machine Alaiya, Azna; Nurdin, Nurdin; Agusniar, Cut
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10977

Abstract

This research aims to analyze the performance of Support Vector Machine (SVM) algorithm in classifying sentiment of e-commerce product reviews on the Tokopedia platform using web scraping data of 571 reviews from the 2024 period. The data includes review text variables, publication dates, and usernames processed through text preprocessing (text cleaning, stopword removal, stemming with Sastrawi), auto-labeling using a lexicon-based approach, and TF-IDF feature extraction with optimal parameters (max_features=5000, ngram_range=(1,2)) resulting in 1,187 features. Data splitting was performed using stratified method with proportions of training (80%) and testing (20%) on 461 reviews from binary classification filtering (positive vs negative). The research results demonstrate that Support Vector Machine with linear kernel achieved excellent performance with accuracy 95.70%, precision 95.89%, recall 95.70%, and F1-score 94.89% on the testing set. Despite the imbalanced dataset characteristics (92.4% positive vs 7.6% negative), SVM effectively handled the classification task by identifying negative sentiment with 100% precision and 42.86% recall, demonstrating its robustness in handling skewed data distribution. TF-IDF feature analysis identified the highest discriminative words such as "suitable", "goods", and "good" that are relevant for classifying consumer sentiment towards e-commerce products. The results indicate that SVM algorithm is highly effective for sentiment classification of e-commerce product reviews, making it suitable for practical implementation in automated sentiment analysis systems for online marketplaces.
PENERAPAN METODE DEMPSTER SHAFER PADA SISTEM PAKAR UNTUK DIAGNOSIS STUNTING Nurdin, Nurdin; Cesilia, Yolinda; Agusniar, Cut
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3S1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3S1.8074

Abstract

Stunting is one of the chronic nutritional problems that affects physical growth, cognitive development, and human productivity in the future. This condition is caused by prolonged nutritional deficiencies and health issues during the early stages of life. This study aims to develop an expert system for diagnosing stunting in toddlers using the Dempster Shafer method, which assists medical personnel in performing early detection based on symptoms and expert belief levels. The Dempster Shafer approach is applied due to its ability to handle uncertainty in data and combine multiple pieces of evidence to produce a rational diagnostic conclusion. The research data were obtained from the Posyandu in Babul Makmur District, Southeast Aceh Regency, consisting of 30 test data samples. The system was developed using the Python programming language, Flask framework, and SQLite database. The testing results show that the system achieved an accuracy rate of 36.66%, with 11 out of 30 test data correctly classified according to expert diagnosis. Although the accuracy remains low, this study demonstrates the potential of the Dempster Shafer method as a foundation for evidence-based diagnostic systems in stunting detection.
Mapping of Flood and Landslide Prone Areas using Composite Mapping Analysis Method Based on Geographic Information System in East Aceh Maulita, Maya; Nurdin, Nurdin; Taufiq, Taufiq
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): 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.v13i6.4483

Abstract

Disaster is an event that causes great losses to the community. Disasters are destructive, very detrimental, and require a long time to recover. To overcome the impact of natural disasters on the community in East Aceh Regency, research is needed related to the mapping system for multi-disaster prone areas (floods and landslides) in East Aceh Regency. The application used for the mapping process is ArcGIS Desktop and the research methodology used for mapping is Composite Mapping Analysis which consists of the process of determining the class of each parameter, determining the weight of each parameter by combining each parameter. The method of combining them consists of a scoring process for each parameter, then overlaying the parameters used, calculating and producing relative weights or spatial means, and combining spatial means to produce a value from the weight of each parameter for floods and landslides. The results of the study showed that the percentage of area for the class very prone to flood disasters was 232,156.13 Ha (42.3%), the vulnerable class had an area of 228,634.01 Ha (41.7%), and the non-vulnerable class had an area of 87,687.40 Ha (16%). The percentage of area for the class that is very vulnerable to landslides is 49,998.13 Ha (9.5%), the vulnerable class has an area of 301,863.93 Ha (57.2%), and the non-vulnerable class has an area of 175,542.56 Ha (33.3%). The contribution of this research is to provide information on disaster-prone areas, causal factors, characteristics of vulnerability to natural disasters such as floods and landslides and provide a basis for more effective decision-making in disaster mitigation and management efforts. This approach offers a new contribution to the technology of mapping and classifying disaster-prone areas.
Application of the Profile Matching Analysis Method in Decision Support Systems for Study Program Recommendations Rasyada, Reza Dian; Nurdin, Nurdin; Fajriana, Fajriana
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): 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.v13i1.3161

Abstract

Currently, many prospective students are still confused about which study program to choose. One of the problematic factors is the lack of references from prospective students about the contents of each study program. This research aims to build a decision support system for study program recommendations using the Profile Matching Analysis method. The benefits of this research can provide final results in the form of recommended study program scores that are most suitable for prospective students. There are 5 assessment criteria used in this research, namely Language Values, Logic/IT Values, Science Values, Practice Values, and Social Values. The methods or stages carried out in this research are: data collection, system flowchart design, application of the Profile Matching Analysis method and system implementation. In this research, the recommendation results were obtained for a student with the name Afni Ruhmini based on the results of system calculations using the Profile Matching Analysis method, obtaining recommendation results for the Public Administration study program with a score = 5.3, the Marine Science study program with a score = 5.9, and the Agribusiness study program with a score = 5.6, the Physics Education study program with a score = 5.7 and the Law study program with a score = 4.8. The Profile Matching Analysis method is very suitable to be applied to solve problems in decision support system research for study program recommendations.
Implementation of Simple Additive Weighting and Profile Matching Methods to Determine Outstanding Students at Universitas Malikussaleh Nurdin, Nurdin; Fikran, Rifzan; Retno, Sujacka
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): 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.v13i4.4176

Abstract

Decision support system (DSS) is a computer-based system used to support data analysis and decision modeling, with the aim of increasing the effectiveness of decisions taken. In this research, SPK is needed to determine Outstanding Students. Through this research, it is hoped that the selection process for outstanding students can be optimized by choosing the evaluation method that best suits the student's characteristics and institutional goals. The results of this research also have the potential to improve the quality of graduates by providing fairer and more objective awards to those who excel. The aim of this research is to design and implement the concept of the Simple Additive Weighting (SAW) and Profile Matching methods in a system for determining outstanding students at Universitas Malikussaleh and to find out the ranking results of the two methods (SAW and Profile Matching) in selecting outstanding students at Universitas Malikussaleh. The research methodology used was literature study, data collection, Simple Additive Weighting and Profile Matching calculations, application design, testing and evaluation. The results obtained from this research are the application of the SAW and Profile Matching methods to determine outstanding students resulting in preferences with the highest score of 1 for the SAW method and the highest score of 5 for the Profile Matching method. These two methods can be applied in selecting outstanding students to help decision making because both this method produces the same best alternative
Co-Authors - Miranda ., Muthmainah Adi Prasetyo Afrilia, Yesy Aidilof, Hafizh Al Kautsar Al Khaidar Alaiya, Azna Alqhifari, Azka Ama Zanati Amalia, Nova Amin Munthoha Aminsyah, Ansharulhaq Ananda Faridhatul Ulva Andri Alfitra Anggara, Aji 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 Fikry , Muhammad 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 Khairul, Khairul Khairuni Khairuni Kurnia, Sri 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 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 Syandriani Harahap Taufik Taufik Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Uci Mutiara Putri Nasution 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