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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) EDUMATIC: Jurnal Pendidikan Informatika JSAI (Journal Scientific and Applied Informatics) IECON: International Economics and Business Conference Media Elektrik
Mursyid Ardiansyah
Institut Teknologi Sains dan Bisnis Muhammadiyah Selayar
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering

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Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb Wahyu Hidayat; Mursyid Ardiansyah; Arief Setyanto
Jurnal Pendidikan Informatika (EDUMATIC) Vol 5, No 1 (2021): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v5i1.3125

Abstract

Traveling activities are increasingly being carried out by people in the world. Some tourist attractions are difficult to reach hotels because some tourist attractions are far from the city center, Airbnb is a platform that provides home or apartment-based rentals. In lodging offers, there are two types of hosts, namely non-super host and super host. The super-host badge is obtained if the innkeeper has a good reputation and meets the requirements. There are advantages to being a super host such as having more visibility, increased earning potential and exclusive rewards. Support Vector Machine (SVM) algorithm classification process by these criteria data. Data set is unbalanced. The super host population is smaller than the non-super host. Overcoming the imbalance, this over sampling technique is carried out using ADASYN and SMOTE. Research goal was to decide the performance of ADASYN and sampling technique, SVM algorithm.  Data analyse used over sampling which aims to handle unbalanced data sets, and confusion matrix used for testing Precision, Recall, and F1-SCORE, and Accuracy. Research shows that SMOTE SVM increases the accuracy rate by 1 percent from 80% to 81%, which is influenced by the increase in the True (minority) label test results and a decrease in the False label test results (majority), the SMOTE SVM is better than ADASYN SVM, and SVM without over sampling.
Analisis Perbandingan Akurasi Algoritma Naïve Bayes dan C4.5 untuk Klasifikasi Diabtes Mursyid Ardiansyah; Andi Sunyoto; Emha Taufiq Luthfi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 5, No 2 (2021): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v5i2.3424

Abstract

Diabetes is a metabolic disease in which blood sugar rises high. If blood sugar is not controlled properly, it can cause a variety of critical diseases, one of which is diabetes. The purpose of this study was to find out the results of comparing the performance values of Naïve Bayes and C4.5 algorithms with 7 different scenarios in the classification of diabetes that will be tested for accuracy, precision, and recall performance. The method used in this study is descriptive, and the source of skunder data obtained from the data of diabetic patients available on Kaggle with the format .csv issued by Ishan Dutta as many as 520 data and 17 fields. The tool used for data analysis is Rapidminer for the process of classification and performance testing of Naïve Bayes algorithm and C4.5 Algorithm. Our results showed that the C4.5 algorithm (scenario 4) had good results in the classification of diabetes compared to Naïve Bayes' algorithm (scenario 2) where the performance of the C4.5 algorithm had an accuracy of 99.03%, precision 100%, and recall 98.18%.
CPU and eGPU Support System Based on Naive Bayes Classification Mursyid Ardiansyah; Wahyu Hidayat; Ema Utami; Suwanto Raharjo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 2 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.63689

Abstract

Central Processing Unit (CPU) and External Graphics Processing Unit (eGPU) technology are known as overclocks which aim to make the device exceed the benchmarks set by the device maker. Until now there is no determination to rank the two hardware within certain limits such as hardware price range and year-by-year. Therefore, it is necessary to process the ranking of the hardware using Simple Additive Weighting (SAW) to obtain a ranking range and determine the weight per type of hardware analyzed. It can be classified using Naïve Bayes to determine results of criteria combination between two hardware to determine possible criteria into "not good" and "good". This classification used to determine probability criteria of choosing a combination of CPU and eGPU hardware. The results of this study are getting the best CPU and eGPU every year using SAW and then classifying it for pricing. In testing conducted on application of Naïve Bayes using 80% of training data has 2776 data and 20% of testing data has 695 data that will be tested for accuracy, precision, recall, and F1-score. For results of tests that have been carried out get 0.78 accuracy results, precision 1, Recall 0.764, and F1-Score 0.866.
Integrating Machine Learning Algorithms into Decision Support Systems for Predicting BBNI Stock Mursyid Ardiansyah; Ari Utomo Saputra
IECON: International Economics and Business Conference Vol. 2 No. 2 (2024): International Conference on Economics and Business (IECON-2)
Publisher : www.amertainstitute.com

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65246/py88s888

Abstract

This study focuses on integrating machine learning models into a Decision Support System (DSS) for predicting BBNI stock prices and generating actionable investment recommendations. The research aims to address the challenges of stock price prediction by evaluating the performance of four models: Multiple Linear Regression (MLR), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). The methodology involves collecting and preprocessing BBNI stock data from 2019 onward, training and evaluating the models using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared, and incorporating the best-performing model into the DSS. The findings reveal that MLR outperformed other models, achieving an MAE of 18.29, RMSE of 23.73, and an R-squared value of 0.9995, indicating high predictive accuracy. RF performed slightly worse but remained competitive, while SVM and ANN exhibited poor results due to limitations in handling complex patterns or tuning issues. The DSS, powered by MLR, successfully generated buy, sell, or hold recommendations based on stock price predictions, with investment simulations confirming its reliability. This study contributes to the field of financial decision-making by demonstrating the effectiveness of MLR in stock price prediction and DSS integration. However, limitations include reliance on historical data and potential model bias. Future research should explore hybrid models and advanced techniques such as deep learning to enhance predictive capabilities. The proposed DSS offers a practical tool for investors, combining robust machine learning insights with user-friendly decision-making support.
Analisis Pemahaman Literasi Digital Siswa Terhadap UU ITE Dan Norma Agama Pada MAN Kepulauan Selayar Al Imran, Abdul Ma'arief; Maro, Muhammad Ihsan; Ardiansyah, Mursyid; Salim, M; Ahmad, Sulistiawati Rahayu
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7595

Abstract

The research focused on MAN Kepulauan Selayar students' level of understanding of the Electronic Information and Transaction Law (UU ITE) and how religious norms influence their attitudes to using digital technology. Selayar Islands, with its distinctive social, cultural, and spiritual characteristics, provides a relevant context for exploring digital literacy in an area with unique educational challenges. Using descriptive methods, a multiple choice test was used to measure students' understanding of the ITE Law. In contrast, a Likert scale questionnaire was used to assess the influence of religious norms on their attitudes. The data obtained were analyzed quantitatively to provide a comprehensive picture of students' digital literacy. The results showed that the average score of students' understanding of the ITE Law was 74, which was classified as a High category. Ranking analysis revealed that class XII students had the highest score of 80.77 (High category), followed by class XI with a score of 74.83 (High category), and class X with a score of 66.4 (Medium category). In addition, the average percentage of students' answers in the questionnaire shows a value of 79.42%, which is classified as Influential in reflecting the influence of religious norms on students' attitudes toward using digital technology. This finding shows that although students' level of understanding of the ITE Law is relatively good, there are differences in the level of understanding between grades. In addition, religious norms have a significant influence on students' attitudes toward the responsible use of digital technology.
PENGEMBANGAN APLIKASI PEMILIHAN PROGRAM STUDI BAGI CALON MAHASISWA PADA SMA SWASTA MUHAMMADIYAH BENTENG Muhammad Ihsan Maro; Mursyid Ardiansyah; Abdul Ma’arief Al Imran; A. Astri Surya Ramadani; Edi Suhardi Rahman
Jurnal Media Elektrik Vol. 20 No. 3 (2023): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v20i3.5573

Abstract

Penelitian ini bertujuan mengembangkan aplikasi untuk pemilihan program studi melalui serangkaian tahapan, mulai dari identifikasi masalah hingga desain dan pengembangan sistem. Aplikasi tersebut memfasilitasi pengguna dalam memilih program studi dengan mempertimbangkan faktor seperti jurusan, rumpun ilmu, sub rumpun ilmu, dan bidang ilmu. Pengembangan front-end dilakukan dengan memanfaatkan teknologi HTML5, CSS, dan JavaScript, sedangkan back-end dikerjakan menggunakan PHP dan MySQLi dengan menerapkan metode Forward Chaining. Uji coba sistem dilakukan melalui pengujian Black Box dengan menguji 5 skenario yang telah ditetapkan, dan hasilnya menunjukkan kelancaran dalam fungsionalitas sistem. Aplikasi diuji oleh 20 siswa dari SMA Swasta Muhammadiyah Selayar, dengan rincian 6 berasal dari jurusan IPA dan 14 dari IPS. Dalam implementasinya, siswa memilih program studi, dan hasilnya mengungkapkan bahwa Ilmu Manajemen dan Ilmu Ekonomi menjadi pilihan terbanyak. Penelitian ini memiliki potensi sebagai referensi bagi pembaca yang ingin mengembangkan aplikasi serupa. Selain itu, konsep aplikasi ini juga dapat diterapkan dalam pengembangan berbasis Android, serta dapat diadaptasi untuk berbagai jurusan, termasuk di lingkungan SMK.
Co-Authors A. Astri Surya Ramadani Abdul Ma’arief Al Imran Al Imran, Abdul Ma’arief Andi Sunyoto Ari Utomo Saputra Arief Setyanto Edi Suhardi Rahman Ema Utami Emha Taufiq Luthfi Maro, Muhammad Ihsan Muhammad Ihsan Maro Muhammad Ihsan Maro Ramadani, A. Astri Surya Salim, M Sulistiawati Rahayu Ahmad Suwanto Raharjo Wahyu Hidayat Wahyu Hidayat