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All Journal CommIT (Communication & Information Technology) Jurnal Indonesia Sosial Teknologi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) IJISCS (International Journal of Information System and Computer Science)
Sani Muhamad Isa, Sani Muhamad
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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 1 
Digital Technology: the Effect of Connected World to Computer Ethic and Family Soewito, Benfano; Isa, Sani Muhamad
CommIT (Communication and Information Technology) Journal Vol 9, No 1 (2015): CommIT Vol. 9 No. 1 Tahun 2015
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v9i1.1654

Abstract

The development of digital technology such as smartphones, tablets and other gadgets grows very rapidly in the last decade so does the development of mobile applications for those mobile systems or smartphones. Unfortunately, those applications often do not specify the age range for their users. This is actually a problem in the world of digital technology and software development. It is not yet known whether the applications is good be used for children or not. Nowadays, parents are faced with the dilemma of allowing their children to use these modern gadgets, which often lead to serious addiction or keeping them in the dark and risk raising ignorant kids. This research shows 80% of respondents agree or strongly agree that the gadget will affect to the development of children social skill. Therefore, in this research, the framework for ethical assessment is introduced and it can be applied to digital technology included gadget and its application in order to mitigate the negative effect of digital technology and gadgets.
Predictive Maintenance Air Conditioner Using Machine Learning Fambudi, Ranggi Tino; Isa, Sani Muhamad
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.414

Abstract

Predictive maintenance will take care of the machine's needs in terms of power loss from damage that lowers performance, operational costs from severe damage, business interruptions from damage that renders the machine unusable, and much more. Almost every home has an air conditioner, the machine that requires constant maintenance of temperature and humidity, especially in offices with servers or control rooms. Preventive and predictive maintenance is necessary to identify the necessary steps for technicians to take when handling an AC before the damage worsens. In this research we implemented and proposed an Air Conditioner detection system using machine learning with three methods, namely K-Nearest Neighbor, Decision Tree, and Random Forest. In order to understand the actual conditions of each AC, we use data sheets that we gathered through surveys with engineering teams at multiple hotels as well as technical teams that handle servers and control rooms. There are 20 features in the gathered data set; however, since only 14 of the features affect the value, extraneous data will be removed. Then the data was divided into two groups, namely 23 AC Failures yes, which means the AC condition is not normal and 110 AC Failures No, which means the AC condition is not damaged. Using the stratified random sample method, 25% of the data will be oversampled. In this study, Kbest and backward elimination were employed for feature selection. The SMOTE approach was then applied for oversampling due to the unbalanced groups. With accuracy values of 91.18%, precision 91.18%, recall 90.90%, and f1-score 90.92%, the Random Forest model with the suggested model outperformed the Decision Tree and KNN models, according to the experimental findings.
Data Mining untuk Memprediksi Animo Masyarakat terhadap Proses Penerimaan Peserta Didik Baru Berek, Anggelina Bete; Isa, Sani Muhamad
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.563

Abstract

Data Mining techniques can now be implemented in all aspects of society because of development in technology. Data Mining can be used in education. There is the possibility of being employed for educational purposes to predict achievements. The involvement of the community is one of the factors that influence the quantity of students at the school. The research project is using Data Mining to predict the factors that impact social engagement. The demographic information used in this study came from the parents of potential candidates. The techniques used are Decision tree, Naïve bayes, and Support Vector Machine. Their accuracy scores are evaluated by a confusion matrix. The results of this study are below: Decision tree 80.16%, Naïve Baye 79.94%, and Support Vector Machine 86.02%.  Based on the comparison results, it can be concluded that the highest accuracy is achieved by using the Support Vector Machine algorithm, while the factor that affects public sentiment is ayah penghasilan.
GOOGLE PLAY STORE USERS COMMENT REVIEW CLASSIFICATION USING SVM CLASSIFIER AND RANDOM FOREST Hadiyasa, Muhammad Rafi; Isa, Sani Muhamad
IJISCS (International Journal of Information System and Computer Science) Vol 7, No 3 (2023): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v7i3.1584

Abstract

In today's digital age, social media stands as a dynamic arena where individuals freely express their thoughts and opinions, from succinct tweets on Twitter to expansive narratives on platforms like Facebook and Instagram. However, amidst this vast sea of user-generated content, a glaring void persists a definitive rating system capable of distilling the nuanced sentiments embedded within these diverse commentaries. This study thus emerges as a pioneering endeavor, poised to bridge this crucial gap in sentiment analysis. Leveraging the transformative potential of the Word2vec methodology in the preprocessing phase, researchers embark on a comprehensive journey to classify comments on a meticulous 1-5 rating scale, thereby unraveling the multifaceted spectrum of sentiments encapsulated within them. Complementing this groundbreaking approach, the Random Forest classification model is harnessed to bolster the analytical prowess of the study. The resultant accuracy score of 60.4% stands as a testament to the study's significant strides towards achieving a deeper understanding of comment sentiment in the realm of social media. However, this is merely the inception of a promising trajectory; the study's findings hold the promise of not only refining sentiment analysis techniques but also revolutionizing diverse sectors, from market research to product development. With this study, the narrative of sentiment analysis transcends the confines of academia, beckoning forth a new era of nuanced comprehension and meaningful engagement within the sphere of social media commentary. As the study concludes, it leaves behind a compelling call to action, inviting further exploration and innovation in this dynamic field.
Application of Data Mining for Prediction of High School Student Graduation Rates Kurniawan, Muhamad; Isa, Sani Muhamad
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 11 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i11.7047

Abstract

The implementation of Data Mining in the education sector aims to develop methods that are able to discover valuable knowledge from data generated in the educational environment. This can be used to increase learning efficiency by paying more attention to students who are predicted to have low grades. However, in its application, each algorithm shows different performance depending on the attributes and dataset used. In this study, a dataset of semester grades and final school exam scores was used. Some of the prediction techniques used are decision trees, support vector machines, and neural networks. Of the four scenarios for the science major at SMAN 2 and SMAN 3 Pangkalpinang with 3 different models, the Mean Squared Error value shows that the test results are in accordance with the testing dataset and can be used as predictions of students' final grades, namely the decision tree model and support vector machine. For the Social Sciences major at SMAN 2 and SMAN 3 Pangkalpinang with 3 different models, the Mean Squared Error value shows that the test results are in accordance with the testing dataset and can be used as a prediction of students' final grades, namely the support vector machine model.
Co-Authors Benfano Soewito Berek, Anggelina Bete Fambudi, Ranggi Tino Hadiyasa, Muhammad Rafi Muhamad Kurniawan, Muhamad