Article Per Year (5 Year)
p-Index From 2020 - 2025
2.092
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) JSI (Jurnal sistem Informasi) Universitas Suryadarma IT (INFORMATIC TECHNIQUE) JOURNAL Journal of Social Research Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Blend Sains Jurnal Teknik DEVOTE: Jurnal Pengabdian Masyarakat Global Hanif Journal of Information Systems Al'Adzkiya International of Computer Science and Information Technology Journal Tsabit
Mulkan Azhari
Universitas Muhammadyah Sumatera Utara, Jl. Kapt. Mukhtar Basri No. 3 Medan, 20238, Medan
Aerospace Engineering Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes Azhari, Mulkan; Situmorang, Zakaria; Rosnelly, Rika
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2937

Abstract

In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm
Analisis Perbandingan Algoritma WP Dan TOPSIS Dalam Menentukan Kandidat Peserta Lomba Kompetensi Siswa Maulia Rahman; Mulkan Azhari
IT (INFORMATIC TECHNIQUE) JOURNAL Vol 10, No 1 (2022): IT JOURNAL APRIL 2022
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/it.10.1.2022.42-55

Abstract

Persaingan adalah bagian dari indikator yang menjadikan sebuah sekolah kejuruan menjadi yang terbaik. Semakin banyaknya siswa yang mengikuti kompetisi tentunya akan berdampak positif bagi sekolah kejuruan itu sendiri. Proses seleksi harus dilakukan karena banyaknya kriteria yang harus dipenuhi oleh siswa sebelum mereka dinyatakan siap bersaing baik secara teori maupun praktek sehingga diperlukan suatu sistem pendukung keputusan yang dapat merekomendasikan calon siswa yang mengikuti lomba kompetensi siswa. Dalam penelitian ini, proses penentuan kandidat peserta lomba kompetensi siswa akan dibahas dengan menganalisis dua metode yaitu metode Weighted Product (WP) dan metode Technique for Order by Similarity to Ideal Solution (TOPSIS) dimana kedua metode akan menganalisis perbandingan dengan masing-masing metode. Selanjutnya menggunakan Euclidean Distance dan pembobotan kriteria Skala Likert untuk menganalisis seberapa besar perbedaan antara kedua metode tersebut. Selain itu, kompleksitas algoritma antara kedua metode dianalisis. Hasil analisis perbandingan menunjukkan bahwa metode WP dengan nilai 0,14281 merupakan metode yang sangat baik karena nilai jaraknya hampir nol dibandingkan dengan metode TOPSIS dengan nilai 0,51238.
Data Mining Dalam Analisis Faktor Drop Out Mahasiswa Menerapkan Algoritma Decision Tree Azhari, Mulkan; Maulana, Halim; Riza, Ferdy
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7379

Abstract

Graduation accuracy is one of the indicators used in assessing the suitability of undergraduate programs as a functional unit of higher education. Knowing the factors that influence student graduation time helps study programs and faculties make decisions to increase the number of students who graduate on time. The purpose of this research is to obtain an overview of the factors that influence students in the accuracy of completing the study time by using the Machine Learning algorithm, namely Decision Tree, which is expected to have high classification efficiency and good description so that it can increase the number of students graduating on time. The methods used to determine student dropout factors are Classification and Regression Tree (CART) and LightGBM. The data used is the data of undergraduate students of Universitas Muhammadiyah Sumatera Utara in 2019. The quality of classification can be read from the accuracy, sensitivity and specification values. The result using CART is 95.1% with the most influencing factors are GPA, faculty, lecture time and predicate while Lightgbm is 83% with the most influencing factors are GPA, gender, lecture time and faculty. Decision tree can be used to determine student dropout factors because of its high accuracy with GPA being the main factor.
Implementation of Fuzzy K-Nearest Neighbor Method in Dengue Disiase Classification Jannah, Aulia; Husaini, Abdillah; Ichsan, Aulia; Azhari, Mulkan
Hanif Journal of Information Systems Vol. 1 No. 2 (2024): February Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v1i2.14

Abstract

Dengue hemorrhagic fever (DHF) is a condition brought on by infection with the dengue virus. DHF is a severe illness with hemorrhagic clinical signs that can result in shock and death. One of the four viral serotypes of the genus Flavivirus is responsible for DHF. DHF symptoms include fever, joint pain, red skin patches, and others that are similar to those of other illnesses. So that there are no errors in illness prediction, strong accuracy and accuracy are required when classifying DHF patients or not. The Fuzzy K-Nearest Neighbor (FKNN) method is used in this study to classify dengue sickness in order to obtain the best classification outcomes. In this investigation, k was searched for eight times, with values of 3,5,7,9,11,13,15, and 20. Each K's accuracy statistics are 75.15, 75.16, 77.58%, 79.51%, 85.01%, 78.14%, and 75.20 percent. K = 13, which has an accuracy score of 85.01%, yields the highest accuracy.
Application Of Propositional Calculus In Determining Vb.Net-Based Student Score Azhari, Mulkan
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 2, No 2 (2021)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v2i2.230

Abstract

In this era of globalization, human life is always followed by various problems that must be taken with a decision. There is no day without a decision being made. There is an opinion that all his behavior is a reflection of the results of the decision-making process through his mind so that humans often make decisions. Through the process of identifying the problem until the selection is the best solution and this is what is called the decision-making process. The method used in this journal is propositional calculus. This method has the concept of determining a statement in which there are premises from which then through a truth table, one's statement can be drawn a conclusion. and after understanding what propositional Calculus is we can apply that material by creating a VB.NET-based app project with the aim of facilitating the work of lecturers or other instructors.
Implementasi Algoritma Decision Tree Untuk Klasifikasi Pemilihan Sekolah Lanjutan Di MTs Al-Hasanah Medan Putra, Aldryan Bhara; Azhari, Mulkan
JSI (Jurnal sistem Informasi) Universitas Suryadarma Vol 12 No 2 (2025): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Universitas Dirgantara Marsekal Suryadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v12i2.1520

Abstract

Algoritma Decision Tree adalah teknik penambangan data yang membantu siswa memilih sekolah menengah secara lebih objektif dengan mengklasifikasikan pilihan sekolah berdasarkan berbagai faktor, termasuk nilai rapor kumulatif nilai keagamaan, umum, dan keterampilan, serta rekomendasi dari guru bimbingan dan konseling. Algoritma Decision Tree bekerja dengan membentuk pola pohon keputusan yang mudah dipahami dan diinterpretasikan, baik oleh siswa maupun pihak sekolah. Data yang digunakan untuk penelitian ini di ambil dari data siswa MTs Al-Hasanah Medan. Dengan variabel meliputi nilai kumulatif raport nilai agama, nilai umum, nilai keterampilan dan rekomendasi guru bimbingan konseling. Hasil dari penelitian ini mayoritas siswa, yakni sebanyak 49 orang (49%), disarankan untuk melanjutkan ke jenjang SMK, berdasarkan hasil klasifikasi data 100 siswa. Sementara itu, jumlah siswa yang direkomendasikan untuk jenjang SMA dan MA cukup merata, yakni masing-masing sebanyak 26 siswa (26%) dan 25 siswa (25%) untuk jenjang SMA dan MA.
Student Data Security Optimization using Multiple Cryptography to Improve E-Campus Services Azhari, Mulkan; Riza, Ferdy; Maulana, Halim
Blend Sains Jurnal Teknik Vol. 3 No. 4 (2025): Edisi April
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/blendsains.v3i4.814

Abstract

Data security is a critical aspect of e-Campus services, which manage student information. However, the risks of data breaches and hacking pose significant challenges, undermining user trust. This study aims to optimize student data security by implementing multiple cryptographic methods that combine Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) algorithms. The research methods include analyzing current security vulnerabilities, developing a security model based on algorithm combinations, and testing its effectiveness through Avalanche Effect measurement, process time evaluation, and user satisfaction surveys. The results indicate that the AES and RSA combination provides stronger protection against security threats, achieving an Avalanche Effect value of 52,36% while ensuring confidentiality, integrity, and data authentication. This implementation also maintains process efficiency, making it suitabble for use in e-Campus environments. This study not only offers a practical solution to enhance data security but also provides implementation guidelines that can be adopted by other higher education institutions.
Analisis Forensik Digital Terhadap Perdagangan Data Pribadi Di Dark Web Menggunakan Osint & Threat Intelligence Dalimunthe, Ahmad Al Qodri Azizi; Azhari, Mulkan
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 1 (2025): Juni 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i1.400

Abstract

Kebocoran data pribadi yang diperjualbelikan di Dark Web menjadi isu yang semakin mengkhawatirkan, terutama setelah kasus yang menimpa Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi (Kemendikbudristek) pada tahun 2024. Penelitian ini bertujuan untuk menganalisis pola perdagangan data pribadi di Dark Web dengan pendekatan forensik digital yang didukung oleh metode Open Source Intelligence (OSINT) dan Threat Intelligence. Penelitian dilakukan dengan studi kasus terhadap data yang dibagikan oleh akun “grepcn” di forum LeakBase dan disebarkan ulang oleh akun “knox” di DarkForums. Proses investigasi dilakukan melalui pemantauan pasif, analisis struktur data dengan tools seperti Python dan NetworkX, serta validasi email menggunakan platform OSINT seperti HaveIBeenPwned dan IntelX. Hasilnya menunjukkan bahwa data pribadi diperjualbelikan dalam format SQL dan disembunyikan di balik sistem berbayar menggunakan mata uang kripto. Sebagian besar data yang dianalisis terbukti valid dan pernah mengalami kebocoran. Penelitian ini menunjukkan bahwa pendekatan gabungan OSINT dan Threat Intelligence dapat digunakan secara efektif untuk mendeteksi dan menganalisis aktivitas perdagangan data pribadi di Dark Web, serta memberikan gambaran awal mengenai ancaman siber yang semakin berkembang.
CATATAN ELEKTRONIK GAWATDARURAT BENCANA (CEGAB) UNTUK OPTIMALISASI MANAJEMEN BENCANA Jundapri, Kipa; Khairani, Ade Irma; Azhari, Mulkan; Febrina, Nadia; Wulandari, Anggy
Devote: Jurnal Pengabdian Masyarakat Global Vol. 4 No. 3 (2025): Devote : Jurnal Pengabdian Masyarakat Global, 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/devote.v4i3.4569

Abstract

Electronic Disaster Emergency (CEGaB) is a platform that can be used to record data on patients/victims undergoing treatment in disaster conditions so that it can help in recording patient/victim data, as well as to get an overview in preparing for future disaster mitigation, so that preparations in facing disasters such as preparing human resources in this case the number and profession of health workers needed, the epidemiology of diseases that appear when a disaster occurs, as well as the need for consumables such as medicines needed if a disaster occurs in the future.
Application of Data Mining to Determine the Performance of Family Planning Field Officers (PLKB) Using the C4.5 Algorithm Nasution, Perdinal; Azhari, Mulkan
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.52

Abstract

The effectiveness of family planning programs is closely related to the performance of Family Planning Field Officers (PLKB). Conventional performance evaluation methods often rely on manual assessments, which may lead to subjectivity and inconsistency. To overcome this issue, data mining techniques can be applied to analyze performance data systematically and objectively. This study employs the C4.5 decision tree algorithm to classify and evaluate the performance of PLKB. The dataset used in this research includes several indicators, such as service coverage, counseling frequency, reporting accuracy, and community participation. Prior to model construction, data preprocessing was performed to handle missing values and normalize attributes. The model performance was evaluated using accuracy, precision, recall, and F-measure. The findings indicate that the C4.5 algorithm successfully classified PLKB performance into three categories: high, medium, and low. The model achieved an accuracy of [insert % if available], demonstrating its effectiveness in identifying key determinants of officer performance. Moreover, the decision tree generated interpretable rules that highlight the most influential attributes affecting PLKB performance. The application of data mining using the C4.5 algorithm provides an objective and efficient method for evaluating PLKB performance. This approach not only enhances decision-making for supervision and training but also contributes to the improvement of family planning program implementation. Future research is suggested to compare the C4.5 algorithm with other classification methods to achieve higher accuracy and generalizability.
Co-Authors Al Khowarizmi Andi Zulherry Dalimunthe, Ahmad Al Qodri Azizi Febrina, Nadia Ferdy Riza Halim Maulana Husaini, Abdillah Ichsan, Aulia Jannah, Aulia Khairani, Ade Irma Kipa Jundapri Maulia Rahman Munadi Munadi Nasution, Perdinal Putra, Aldryan Bhara Rachmad Suhanda Rika Rosnelly, Rika Situmorang, Zakaria Winda Yulia Wulandari, Anggy Zulkarnain Zulkarnain