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All Journal Jurnal SOLMA JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOISIE (Journal Of Information Systems And Informatics Engineering) JURNAL PIONIR Journal Zetroem Jurnal Intelek Dan Cendikiawan Nusantara CSRID
Azrai Sirait
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Perancangan Sistem E-Arsip Surat Masuk dan Keluar di BKAD Kabupaten Asahan Abdul Aziz Ardana; Azrai Sirait
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 2 No. 5 (2025): Oktober - November 2025
Publisher : PT. Intelek Cendikiawan Nusantara

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

Abstract

Pengelolaan arsip surat masuk dan surat keluar pada Bagian Umum Badan Keuangan dan Aset Daerah (BKAD) Kabupaten Asahan saat ini masih dilakukan secara konvensional. Pencatatan data surat masih mengandalkan buku agenda fisik dan penyimpanan dokumen dilakukan secara manual dalam lemari arsip. Metode ini memiliki berbagai kelemahan, antara lain lambatnya proses temu kembali (pencarian) dokumen, risiko tinggi terhadap kerusakan atau kehilangan arsip fisik, serta inefisiensi dalam pembuatan laporan rekapitulasi. Penelitian ini bertujuan untuk merancang dan membangun Sistem Informasi E-Arsip berbasis web guna mengatasi permasalahan tersebut. Metode penelitian yang digunakan adalah observasi partisipatif dan studi literatur. Sistem dirancang menggunakan pemodelan UML (Unified Modeling Language) dan dikembangkan dengan bahasa pemrograman PHP Native serta database MySQL. Hasil dari penelitian ini adalah sebuah sistem E-Arsip yang memiliki fitur pencatatan surat, upload dokumen digital, manajemen kategori, pencarian cepat, dan cetak laporan otomatis. Berdasarkan pengujian Black Box, sistem ini terbukti valid dan mampu meningkatkan efisiensi serta akuntabilitas pengelolaan arsip di BKAD Kabupaten Asahan.
Pelatihan Media Pembelajaran Aplikasi Feliz Terintegrasi Framework MAS Darul Falah Saragih, Sri Rahmah Dewi; Mapilindo, Mapilindo; Sirait, Azrai; Purba, Oktaviana Nirmala
Jurnal SOLMA Vol. 14 No. 3 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i3.21059

Abstract

Background: The transformation of education in the digital era requires educators to master innovative, practical, and integrated learning media, in line with global standards such as the UNESCO ICT Competency Framework for Teachers (2018). In reality, 35 teachers at MAS Darul Falah still face limitations in utilizing digital applications. To address this need, the community service team implemented a training program on the use of the Feliz application, integrated with a learning framework. Method: The activity was carried out using the Experiential Learning method, emphasizing hands-on practice. Although the intensive training session was conducted in one full day, the overall mentoring program ran for seven months to ensure sustainability. Evaluation indicators used quantitative instruments through pre-test and post-test on three competence aspects and structured observations. Results: The results showed a significant improvement in teachers’ skills in designing interactive learning media, with capability increasing from 15% to 70% (a 55% improvement). This concrete contribution was measured and demonstrated through the measurable increase in competence. The improvement in teachers’ self-confidence (from 25% to 80%) was analyzed using the concept of Self-Efficacy (Bandura). Conclusion: This program not only enhanced teachers’ competencies but also provided measurable contributions to the quality of the teaching and learning process at MAS Darul Falah.
SISTEM PAKAR PENDETEKSI PENYAKIT KULIT MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER Azhar, Abidin; Sirait, Azrai
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5868

Abstract

Abstract: One application of artificial intelligence in the healthcare sector is the development of expert systems capable of emulating the reasoning process of medical experts in diagnosing diseases based on observed symptoms. Skin diseases are common health problems caused by various factors, including fungal, bacterial, viral, parasitic infections, and immune system disorders. The identification of skin diseases generally requires direct consultation with a medical specialist. However, in practice, several obstacles remain, such as patients’ reluctance to seek medical consultation, limited access to healthcare services, long distances to medical facilities, and relatively high consultation and treatment costs. Through artificial intelligence, computers can perform tasks previously limited to humans, including decision-making processes modeled on human reasoning. One method that can be applied is the Naïve Bayes Classifier. Therefore, this study proposes an expert system for the early diagnosis of skin diseases using the Naïve Bayes Classifier method to provide a fast and efficient alternative solution for preliminary identification. Keywords: expert system, skin disease, naïve bayes classifier, artificial intelligence, diagnosis. Abstrak: Salah satu penerapan kecerdasan buatan dalam bidang kesehatan adalah pengembangan sistem pakar yang mampu meniru cara berpikir seorang ahli dalam mendiagnosa penyakit berdasarkan gejala yang muncul. Penyakit kulit merupakan salah satu permasalahan kesehatan yang umum terjadi dan dapat disebabkan oleh berbagai faktor seperti infeksi jamur, bakteri, virus, parasit, maupun gangguan sistem imun. Proses identifikasi penyakit kulit pada umumnya memerlukan konsultasi langsung dengan dokter spesialis. Namun demikian, dalam praktiknya masih terdapat berbagai kendala, seperti rasa malu pasien untuk berkonsultasi, keterbatasan akses layanan kesehatan, jarak lokasi praktik dokter yang relatif jauh, serta biaya pemeriksaan dan pengobatan yang cukup tinggi. Dengan kecerdasan buatan komputer dapat melakukan hal-hal yang sebelumnya hanya dapat dilakukan oleh manusia, dan Manusia dapat menjadikan komputer sebagai pengambil keputusan berdasarkan cara kerja otak manusia dalam mengambil keputusan. Salah satu metode yang dapat digunakan adalah Metode Naive Bayes Classfifier. Penyakit kulit dapat disebabkan oleh jamur, virus, kuman, parasit hewani, infeksi bakteri dan lain-lain. Mengidentifikasi penyakit kulit biasanya kita harus ke dokter, namun masih mengalami kendala dalam menangani pengidentifikasi penyakit hal itu terkadang dipengarui oleh masyarakat terkadang merasa malu untuk mengkonsultasikan penyakit kulitnya ke dokter karena tanda-tanda penyakit kulit sudah mulai tampak, biaya konsultasi dan obat yang tergolong mahal, lokasi praktek dokter jauh. Kata kunci: sistem pakar, penyakit kulit, naïve bayes classifier, kecerdasan buatan, diagnosa.
PENERAPAN ALGORITMA COSINE SIMILARITY UNTUK MENGIDENTIFIKASI KEBUTUHAN BELAJAR MURID DI SEKOLAH BERBASIS DATA ANALISIS Aruni Muprida; Azrai Sirait
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4793

Abstract

Abstract: In an educational context, Cosine Similarity can be used to compare students' learning profiles, for example based on exam results, assignments, or material preferences, to determine the level of similarity between them. The results of this calculation can help teachers design more personalized learning programs, such as providing additional material for students who are struggling or special challenges for students who have already mastered the material well. The data used in this study were learning outcomes, grades, interests, and academic activities. The purpose of this study was to implement the Cosine Similarity algorithm to identify students' learning needs. The test results for four students can be seen in Andi's final score of 0.932. The highest profile: "Needs Math Help." Recommendation: Basic arithmetic practice and reinforcement of numerical logic are recommended. Budi's score of 0.992. The highest profile: "Needs Language Support." Recommendation: Reading and writing simple sentences are recommended. Siska's score of 0.996. The highest profile: "Needs Language Support." Recommendation: Reading and writing simple sentences are recommended. Feri's score of 0.985. Highest profile: "Needs Science/Social Studies Strengthening." Recommendation: Simple experiments are recommended to improve science/social studies understanding. The designed system successfully displays analysis results in a format that is easy for teachers to understand, namely through data visualization in the form of tables, graphs, or cluster diagrams. Keywords: Cosine Similarity Algorithm, Identifying Student Learning Needs, Data Analysis. Abstrak: Dalam konteks pendidikan, Cosine Similarity dapat digunakan untuk membandingkan profil belajar murid, misalnya berdasarkan hasil ujian, tugas, atau preferensi materi, guna menentukan tingkat kesamaan di antara mereka. Hasil perhitungan ini dapat membantu guru dalam merancang program pembelajaran yang lebih personal, seperti pemberian materi tambahan bagi murid yang kesulitan atau tantangan khusus bagi murid yang sudah menguasai materi dengan baik. Data yang dugunakan dalam penelitian ini ialah data hasil belajar, nilai, minat, dan aktivitas akademik. Tujuan dalam peneltian ini ialah mengimplementasikan algoritma Cosine Similarity untuk mengidentifikasi kebutuhan belajar murid. hasil pengujian terhadap 4 siswa dapat dilihat atas nama andi hasil nilai akhir 0.932. Profil tertinggi: “Butuh Bantuan Matematika” Rekomendasi: Disarankan latihan berhitung dasar dan penguatan logika angka. Atas nama Budi dengan nilai 0.992. Profil tertinggi: “Butuh Dukungan Bahasa”. Rekomendasi:  Disarankan latihan membaca dan menulis kalimat sederhana. Atas nama Siska dengan nilai 0.996. Profil tertinggi: “Butuh Dukungan Bahasa”. Rekomendasi:  Disarankan latihan membaca dan menulis kalimat sederhana. Atas nama Feri dengan nilai 0.985. Profil tertinggi: “Butuh Penguatan IPA/IPS”. Rekomendasi: Disarankan eksperimen sederhana untuk meningkatkan pemahaman IPA/IPS. Sistem yang dirancang berhasil menampilkan hasil analisis dalam bentuk yang mudah dipahami oleh guru, yaitu melalui visualisasi data berupa tabel, grafik, atau diagram cluster. Kata Kunci: Algoritma Cosine Similarity, Mengidentifikasi Kebutuhan Belajar Murid, Data Analisis.
SISTEM PAKAR PENDETEKSI PENYAKIT KULIT MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER Abidin Azhar; Azrai Sirait
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5868

Abstract

Abstract: One application of artificial intelligence in the healthcare sector is the development of expert systems capable of emulating the reasoning process of medical experts in diagnosing diseases based on observed symptoms. Skin diseases are common health problems caused by various factors, including fungal, bacterial, viral, parasitic infections, and immune system disorders. The identification of skin diseases generally requires direct consultation with a medical specialist. However, in practice, several obstacles remain, such as patients’ reluctance to seek medical consultation, limited access to healthcare services, long distances to medical facilities, and relatively high consultation and treatment costs. Through artificial intelligence, computers can perform tasks previously limited to humans, including decision-making processes modeled on human reasoning. One method that can be applied is the Naïve Bayes Classifier. Therefore, this study proposes an expert system for the early diagnosis of skin diseases using the Naïve Bayes Classifier method to provide a fast and efficient alternative solution for preliminary identification. Keywords: expert system, skin disease, naïve bayes classifier, artificial intelligence, diagnosis. Abstrak: Salah satu penerapan kecerdasan buatan dalam bidang kesehatan adalah pengembangan sistem pakar yang mampu meniru cara berpikir seorang ahli dalam mendiagnosa penyakit berdasarkan gejala yang muncul. Penyakit kulit merupakan salah satu permasalahan kesehatan yang umum terjadi dan dapat disebabkan oleh berbagai faktor seperti infeksi jamur, bakteri, virus, parasit, maupun gangguan sistem imun. Proses identifikasi penyakit kulit pada umumnya memerlukan konsultasi langsung dengan dokter spesialis. Namun demikian, dalam praktiknya masih terdapat berbagai kendala, seperti rasa malu pasien untuk berkonsultasi, keterbatasan akses layanan kesehatan, jarak lokasi praktik dokter yang relatif jauh, serta biaya pemeriksaan dan pengobatan yang cukup tinggi. Dengan kecerdasan buatan komputer dapat melakukan hal-hal yang sebelumnya hanya dapat dilakukan oleh manusia, dan Manusia dapat menjadikan komputer sebagai pengambil keputusan berdasarkan cara kerja otak manusia dalam mengambil keputusan. Salah satu metode yang dapat digunakan adalah Metode Naive Bayes Classfifier. Penyakit kulit dapat disebabkan oleh jamur, virus, kuman, parasit hewani, infeksi bakteri dan lain-lain. Mengidentifikasi penyakit kulit biasanya kita harus ke dokter, namun masih mengalami kendala dalam menangani pengidentifikasi penyakit hal itu terkadang dipengarui oleh masyarakat terkadang merasa malu untuk mengkonsultasikan penyakit kulitnya ke dokter karena tanda-tanda penyakit kulit sudah mulai tampak, biaya konsultasi dan obat yang tergolong mahal, lokasi praktek dokter jauh. Kata kunci: sistem pakar, penyakit kulit, naïve bayes classifier, kecerdasan buatan, diagnosa.
ANALISIS JARINGAN SYARAF TIRUAN UNTUK KLASIFIKASI KELULUSAN MAHASISWA BERDASARKAN DATA AKADEMIK MENGGUNAKAN ALGORITMA PERCEPTRON Dini Farhatun; Azrai Sirait
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6556

Abstract

This study aims to analyze and classify student graduation based on academic data using the Artificial Neural Network (ANN) method with the Perceptron algorithm. The background of this research is the low rate of on-time student graduation caused by differences in students’ academic achievements during their studies. The academic data used in this research include Grade Point Average (GPA), total passed credits, and total failed credits. The study was conducted using data from Informatics Engineering students of Universitas Asahan class of 2021.The research method used is a quantitative method with stages including data preprocessing, classification target determination, weight initialization, perceptron training process, activation function calculation, error evaluation, and weight updating until convergence is achieved. The dataset was divided into training data and testing data to evaluate the model’s ability to classify student graduation into two categories, namely on-time graduation and delayed graduation. The system was developed using the PHP programming language and MySQL database and designed using Unified Modeling Language (UML).The results of this study indicate that the Artificial Neural Network method with the Perceptron algorithm is capable of classifying student graduation based on academic data effectively. The perceptron model is able to recognize the relationship patterns between GPA, passed credits, and failed credits variables toward student graduation status. The developed system can also assist the study program in academic evaluation and decision-making processes related to improving the quality of student graduation.
Co-Authors Abdul Aziz Ardana Abdullah, Iqbal Abidin Azhar Adinda, Aura Agung Purnomo Annisa Fitri Ani Aruni Muprida Azhar, Abidin Dini Farhatun Emi Dea Farhan Graha Hidayat MAPILINDO Muhammad Siddik Muprida, Aruni purba, oktaviana nirmala Rahma, Fadillah Aulia Samsir Samsir, Samsir Saragih, Sri Rahmah Dewi Siddik, Muhammad Syahfitri, Ayu