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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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Journal : Journal Of Artificial Intelligence And Software Engineering

 1 
Implementation of Profile Matching Analysis Method for Decision-Making in Online Learning for Homeschooling Student Jikti Khairina; Nurdin Nurdin; Muhammad Nasir
Journal of Artificial Intelligence and Software Engineering Vol 4, No 2 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i2.6126

Abstract

In a homeschooling learning system, adjustment between learning methods and student characteristics is very important to achieve optimal learning outcomes. Online learning provides flexibility for homeschooled students, but determining the most appropriate learning method according to the student's profile is still a challenge. In the context of homeschooling, where an individual approach is needed, the application of the Profile Matching method in decision-making for an online learning system allows for the personalization of education according to student characteristics, where this method provides recommendations for the most appropriate learning methods based on student profiles, including learning styles, cognitive abilities, and learning preferences. By comparing the profile of students' competencies and learning styles against predetermined criteria, the system can provide recommendations for appropriate learning methods. The results of this study indicate that the Profile Matching Analysis method can improve learning effectiveness and facilitate personalization of the learning process.
Comparison of the Performance of Fuzzy Tsukamoto and Fuzzy Mamdani in an Internet of Things Based Grape Greenhouse Control System Rusadi, Athirah; Ula, Munirul; Daud, Muhammad; Nurdin, Nurdin; Hasibuan, Arnawan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6936

Abstract

The application of Internet of Things in agriculture, particularly in grape greenhouses, enables automated environmental control to enhance efficiency and crop yield. This study compares the performance of two fuzzy logic methods, Fuzzy Mamdani and Fuzzy Tsukamoto, in a temperature and humidity control system based on IoT using the DHT22 sensor. The system is designed to automate irrigation via actuators based on sensor data. Performance evaluation was conducted using RMSE, MAE, and standard deviation metrics. The results show that the Tsukamoto method achieved lower RMSE 2.6928, MAE 2.2625, and standard deviation 1.1080 compared to the Mamdani method, which recorded RMSE of 2.9039, MAE of 2.3947, and standard deviation of 1.9268. However, a paired t-test yielded a p-value of 0.0690 0.05, indicating no statistically significant performance difference. Thus, while Fuzzy Tsukamoto appears superior in metrics, both methods are considered equally effective for controlling environmental conditions in grape greenhouses.The application of Internet of Things in agriculture, particularly in grape greenhouses, enables automated environmental control to enhance efficiency and crop yield. This study compares the performance of two fuzzy logic methods, Fuzzy Mamdani and Fuzzy Tsukamoto, in a temperature and humidity control system based on IoT using the DHT22 sensor. The system is designed to automate irrigation via actuators based on sensor data. Performance evaluation was conducted using RMSE, MAE, and standard deviation metrics. The results show that the Tsukamoto method achieved lower RMSE 2.6928, MAE 2.2625, and standard deviation 1.1080 compared to the Mamdani method, which recorded RMSE of 2.9039, MAE of 2.3947, and standard deviation of 1.9268. However, a paired t-test yielded a p-value of 0.0690 0.05, indicating no statistically significant performance difference. Thus, while Fuzzy Tsukamoto appears superior in metrics, both methods are considered equally effective for controlling environmental conditions in grape greenhouses.
Information Systems And Information Technology Strategies In The EMIS (Education Management Information System) Khaidar, Al; Azzanna, Maghriza; Rahmad, Rahmad; Hasibuan, Arnawan; Daud, Muhammad; Nurdin, Nurdin
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.v5i3.7639

Abstract

Perkembangan teknologi informasi telah memengaruhi pengelolaan data pendidikan melalui sistem informasi manajemen, salah satunya Education Management Information System (EMIS). Penelitian ini bertujuan untuk menganalisis efektivitas implementasi EMIS di MAN 1 Aceh Timur serta faktor-faktor yang memengaruhi keberhasilannya. Metode penelitian menggunakan pendekatan kualitatif interaktif dengan studi kasus, melibatkan kepala madrasah, operator EMIS, dan pihak terkait sebagai informan. Analisis dilakukan menggunakan metode SWOT dan value chain untuk mengevaluasi kekuatan, kelemahan, peluang, dan ancaman implementasi sistem. Hasil penelitian menunjukkan EMIS memiliki potensi meningkatkan efektivitas pengelolaan data, integrasi informasi, dan mendukung pengambilan keputusan. Namun, sistem mengalami kendala teknis, terutama gangguan server dengan frekuensi bervariasi setiap bulan, puncaknya terjadi pada Maret dan Juli masing-masing 5 kali, dengan durasi rata-rata meningkat dari 1,8 jam di Januari menjadi 2,5 jam di Juli dan terendah 1,0 jam di April. Evaluasi menekankan perlunya peningkatan infrastruktur, pelatihan operator, dan koordinasi antar pihak terkait untuk mengoptimalkan kinerja EMIS di masa depan.
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
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