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All Journal ComEngApp : Computer Engineering and Applications Journal International Journal of Renewable Energy Development Journal of ICT Research and Applications CESS (Journal of Computer Engineering, System and Science) Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jiko (Jurnal Informatika dan komputer) KOMPUTEK Charity : Jurnal Pengabdian Masyarakat Jurnal Nasional Teknik Elektro dan Teknologi Informasi eProceedings of Engineering Dike International Journal of Renewable Energy Development
Danang Triantoro Murdiansyah
Telkom University
Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Analisis Sentimen pada Steam Review Menggunakan Multinomial Naïve Bayes dan Seleksi Fitur Gini Index Text Haditira, Ragil; Murdiansyah, Danang Triantoro; Astuti, Widi
KOMPUTEK Vol. 8 No. 2 (2024): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v8i2.2981

Abstract

Video game is one of the entertainment medias chosen by most people today, many of which are played through computer devices. On computer devices, many video games are obtained through one of the game distribution platforms, namely Steam. However, Steam has several shortcomings, including those related to Steam reviews. On Steam reviews, you can see the rating of the game, but the rating does not really show the actual quality or condition of the game. As one example, there are users who give a high rating to a game, but in the comments column the user actually mentions the shortcomings of the game. To reduce or anticipate unclear reviews for users who want to try or buy the game, sentiment analysis on reviews is used. In this research, the output produced is information on the results of sentiment classification in filtering reviews, using the Multinomial Naïve Bayes algorithm and combined with the Gini Index feature selection. Sentiment classification is divided into two classes, namely recommended and not recommended classes. In this study, to test the sentiment classification system, a dataset containing reviews in the form of review sentences from Steam is used. The test results using Multinomial Naïve Bayes and Gini Index, can achieve the best accuracy of 60.29%.
Implementation of Naïve Bayes and Gini Index for Spam Email Classification Imadudin, Fikri Rozan; Murdiansyah, Danang Triantoro; Adiwijaya
Indonesian Journal on Computing (Indo-JC) Vol. 6 No. 1 (2021): April, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.1.452

Abstract

Email is a medium of information that is still frequently used by people today. At the moment email still has an endless problem that is spam email. Spam email is an email that can pollute, damage or disturb the recipient. In this study, we show the performance and accuracy of Multinomial Naïve Bayes (MNNB) and Complete Gini-Index Text (GIT) for use in spam email filtering. In this study, we used 6 cross-validations as testers for the built classification machines. We found that the average yield can exceed Multinomial Naïve Bayes without using feature selection which only uses 80000 features with a difference of 0.39%. Feature selection also increases speed during classification and can reduce features that are less relevant to the category to be classified.
Implementation of K-Means++ Algorithm for Store Customers Segmentation Using Neo4J Chaerudin, Arief; Murdiansyah, Danang Triantoro; Imrona, Mahmud
Indonesian Journal on Computing (Indo-JC) Vol. 6 No. 1 (2021): April, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.1.547

Abstract

In the era of data and information, data has become one of the most useful and desirable things. Data can be useful information if the data is processed properly. One example of the results of data processing in business is by making customer segmentation. Customer segmentation is useful for identifying and filtering customers according to certain categories. Analysis of the resulting segmentation can produce information about more effective target market, more efficient budget, more accurate marketing or promotion strategies, and much more. Since segmentation aims to separate customers into several categories or clusters, a clustering algorithm can be used. In this research, customer segmentation is carried out based on the value of income and value of expenditure. The categorization method that will be used for this research is to use the K-Means ++ algorithm which is useful for determining clusters of the given data. In this study, the implementation of K-Means ++ is carried out using Neo4J. Then in this research, a comparison of K-Means ++ and K-Means is carried out. The result obtained in this study is that K-Means ++ has a better cluster than K-Means in term of silhouette score parameter.
Classification Model of Consumer Question about Motorbike Problems by Using Naïve Bayes and Support Vector Machine Wicaksana, Ekky; Murdiansyah, Danang Triantoro; Kurniawan, Isman
Indonesian Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.561

Abstract

The motorbike plays an important role in supporting daily activity. The motorbike is known as one of the transportation modes that is frequently used in Indonesia. The number of motorbikes used in Indonesia is continuously increasing time by time. Hence, the occurrence of motorbike problems can affect community activity and disturb the economic condition in society. Since the problem of the motorbike can occur at any time, a prevention action is required by providing an online consultation platform. However, a classification model is required to handle a wide range of questions about the motorbike problem. By classifying those questions into a specific class of problems, the solution can be delivered to the consumer faster. In this study, we developed prediction models to classify consumer questions. The data set was collected from consumer questions regarding motorbike problems that are commonly occurring. The model was developed using two machine learning algorithms, i.e., Naïve Bayes and Support Vector Machine (SVM). Text vectorization was performed by using the n-gram and term frequency-inverse document frequency (TF-IDF) method. The results show that the SVM model with the uni-trigram model performs better with the value of accuracy and F-measure, which are 0.910 and 0.910, respectively.
Sistem Deteksi Malware Menggunakan Information Gain dan Decision Tree Aditya Pratama, Rangga; Murdiansyah, Danang Triantoro
CESS (Journal of Computer Engineering, System and Science) Vol. 10 No. 2 (2025): Juli 2025
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v10i2.67170

Abstract

Malicious Software, atau yang dikenal dengan malware, merupakan perangkat lunak berbahaya yang dapat menyebabkan hal-hal yang tidak diinginkan, seperti kehilangan data, pencurian informasi, penyebaran data pribadi, dan penyalahgunaan informasi penting. Pada penelitian ini, metode yang digunakan untuk deteksi malware adalah metode Decision Tree untuk klasifikasi dan metode Information Gain untuk seleksi fitur. Metode Decision Tree mempermudah dalam melacak dan memahami keputusan dengan struktur pohonnya, sementara Information Gain membantu dalam memilih fitur yang paling relevan dan informatif. Penelitian ini bertujuan untuk meningkatkan akurasi dan efisiensi dalam mendeteksi malware Portable Executable (PE) yang menargetkan file eksekusi pada sistem operasi Windows. Dataset yang digunakan adalah dataset SOMLAP (Swarm Optimization and Machine Learning Applied to PE Malware Detection), yang mengandung 51409 sampel file excutable Windows yang diekstrasi, yang terdiri dari file benign (non malware) dan file malware. Hasil penelitian menunjukkan bahwa pada proporsi data 90:10, metode pemilihan 20 fitur dengan Information Gain berhasil meningkatkan efisiensi dan efektivitas dalam mendeteksi malware PE dengan rata-rata akurasi 99,3% dan rata-rata waktu pemrosesan yang diperlukan sebesar 32 detik dibandingkan dengan metode terbaik pada penelitian sebelumnya, yaitu Ant Colony Optimization dan Decision Tree yang memiliki rata-rata akurasi 98,8% dan rata-rata waktu pemrosesan sebesar 43 detik.
Co-Authors Aditya Pratama, Rangga Adiwijaya Aldi Riyadi Ramadhan Ali Helmut Andrian Yoga Pratama Annisa Aditsania Arief Chaerudin Aura Sukma Andini Barini Harahap Berlian Kaida Palma Chaerudin, Arief Dany Dwi Prayoga Dede Tarwidi Della Alfarydy Akbar Devi Pratami Dwi Nugroho Dwi Yanita Apriliyana Ekky Wicaksana Emir Husni Emir Husni, Emir Favian Dewanta Felix Octavianus Hasudungan Ferdian Yulianto fh Nhita Fhira Nhita Fikri Rozan Imadudin Gege Safet Yanto Raharjo Gery Nugroho Haditira, Ragil Haga Simada Ginting Hana Rufaidha Ika Arum Puspita Imadudin, Fikri Rozan Isman Kurniawan Jodi Noordiansyah Jondri Jondri Kemas M Lhaksmana Kemas Muslim Lhaksmana Kemas Rahmat Saleh Wiharja Litasari Widyastuti, Litasari Lukito Agung Waskito M Ikhsan Perdana Putra M. Hady Setiawan Mahendra, Muhammad Hafizh Mahmud Imrona Merry Sofhia Tambunan Miranti Andhita Scantya Muhamad Paisal Hanip Muhammad Ramadhan Gandidi Muhammad Zakiyullah Romdlony Narwan Ginanjar Narwan Ginanjar, Narwan Nida Mujahidah Azzahra Nurul Ikhsan Ragil Haditira Reza Rendian Septiawan Rian Febrian Umbara Sahlya Handayati Sugeng Hadi Wirasna Tiwa Ramdhani Umar Ali Ahmad Wicaksana, Ekky Widi Astuti Widi Astuti Youga Pratama Yuwantoro Mukhlisin ZK Abdurahman Baizal