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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TEKNOLOGIA Jurnal Linguistik Komputasional Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Journal of Data Science and Its Applications Charity : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer Prosiding Konferensi Nasional PKM-CSR Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Indonesia Sosial Teknologi eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Dike Jurnal INFOTEL CSRID LOGIC: Jurnal Penelitian Informatika

Kemas Muslim Lhaksmana

Universitas Telkom, Indonesia

Author-ID : 2489655

Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Classification Analysis of Waiting Period for Telkom University Alumni to Get Jobs Using Decision Tree and Support Vector Machine Annisa Miranda; Kemas Muslim Lhaksamana
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1963

Tracer analysis is one of the ways to increase a university's accreditation. Tracer studies, also known as graduate surveys, are beneficial for enhancing learning and developing university curricula. The period it takes graduates to secure employment is a measure of their quality. The sooner graduates obtain a job, the higher their perceived quality. Conversely, if it takes graduates longer to find employment, their quality is deemed lower. To gain new knowledge from the tracer study dataset regarding the relationship between university contribution and alumni capability in the job market, in this study, data mining techniques are used to determine what factors influence the length of time it takes college graduates to find employment. This classification model contains a total of 2288 data instances from the categorical type of dataset. The features are selected using chi-square. Two classification algorithms, Decision Tree and Support Vector Machine, are compared for the best model. This study also used hyperparameter tuning to improve accuracy. The results show decision tree produces higher accuracy compared to the support vector machine. The accuracy obtained from the decision tree model is 55.02% and increased to 65.06% after hyperparameter tuning. Meanwhile, the support vector machine brought an accuracy of 60.40% and increased to 62.15% after hyperparameter tuning. Factors that affect the classification of the alumni waiting period in getting a job in this study are sex, faculty of the study field, department of the study field, study period, company specification, company category, and work location.

Prediction Retweet Using User-Based and Content-Based with Artificial Neural Network-Harmony Search Rizky Ahmad Saputra; Jondri Jondri; Kemas Muslim Lhaksmana
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4079

Online social networking services allow users to post content in the form of text, images or videos. Twitter is a microblogging social networking service that enables its users to send and read text-based messages of up to 140 characters. Retweet is one of the features in Twitter that is important in disseminating information, popular tweets reflect the latest trends on Twitter, the main mechanism that encourages information dissemination is the possibility for users to re-share content posted by their social connections, then it can flow throughout the system. Retweets happen when someone republishes or forwards a post to their homepage and personal profile. Most retweets are credited to the original author of the original post. The retweet prediction system uses an Artificial neural network optimized for Harmony search with tweets about the Jakarta-Bandung Fast Train, which shows the best results when the oversampling method has been carried out with an f1 score of 96.8%.

A Multi-Label Classification of Al-Quran Verses Using Ensemble Method and Naïve Bayes Choirulfikri, Muhammad Rizqi; Lhaksamana, Kemas Muslim; Faraby, Said Al
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Al-Quran is the holy book as a guide and also a source of law for muslims. Thus, understanding and studying Al-Quran is very important for muslims. To make it easier for muslims to understand and study the Qur'an, it is necessary to classify the verses of the Al-Qur'an. This study built a system that can perform multi-label classification of Al-Quran verses. Multi-label means that the classification will divide each verse of the Al-Quran into more than 1 topic. The model is built using the ensemble method by combining several Naïve Bayes algorithms. The ensemble method was chosen because research with different datasets can obtain good performance. The naïve Bayes algorithm was also chosen because it has a simple calculation so it requires a fairly short computation time. The preprocessing step is also carried out to see the comparison of performance results. To measure the performance of the system that has been built, the calculation of hamming loss is used. Based on the experimental results with several testing scenarios, the best performance results are obtained by combining Multinomial NB and Bernoulli NB with a hamming loss value of 0.1167. Thus, the use of the ensemble method can improve performance compared to without the ensemble method. This research can also of course build a multi-label classification model for the verses of Al-Quran with the ensemble method

Supervised Learning Approaches for Nested People Entity Extraction in Indonesian Translated Quran Dzidny, Dimitri Irfan; Bijaksana, Moch Arif; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Since the Quran is the primary holy book for Muslims, information extraction research on Quranic texts, especially in a form of People Entity Extraction, is an important task for further Quran and Tafseer understanding. The challenges in extracting people entities from the Quranic text is that many verses have a complex structure, such as nested entities, making it crucial to build a system that can extract the entity automatically, accurately, and quickly. People Entity Extraction on Quran itself is a task that aims to extract people entities in a sentence or verse, such as the name of a person, the name of a group, etc. on the Quranic texts. Example of input taken from snippet Surah Al-Baqarah verse 46 which reads “Those who believe that they will meet their Lord and that they will return to him” from that input the people entity extraction system is expected can identify people entities i.e. “Those who believe that they will meet their Lord”. Currently, People Entity Extraction research for the Quran has not been widely carried out, only a few algorithms with scattered results have been conducted. In this research, we will use several supervised models which are Conditional Random Field (CRF), BiLSTM-CRF, and a pre-trained deep learning model based on IndoBERT transformers. We apply and perform a comparative analysis for the performance of those several models. We found out that deep learning based model, namely BiLSTM-CRF perform best at extracting people entities, whilst probabilistic based model, namely CRF, had difficulty in extracting people entities, specifically nested people entities.

Twitter Sentiment Analysis of Kanjuruhan Disaster using Word2Vec and Support Vector Machine Rizky, Fariz Muhammad; Jondri, Jondri; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3612

The Kanjuruhan disaster on 1 October 2022, gained the peoples attention. People share their thoughts on social media. Their posts contain a variety of perspectives. Sentiment analysis is possible to use on a dataset of people's posts. This final project applies the supervised learning Support Vector Machine (SVM) method with feature expansion using Word2Vec and TF-IDF as weighting. Three SVM kernels—rbf, linear, and polynomial—are applied. Three split data techniques and two different types of training data are used to train each kernel. Training data with oversampling and training data without oversampling are the two types of training data. The best result gained from using rbf kernel, split ratio 70:30, and oversampling. From it, oversampling trained model have relatively stable in every split rasio and kernel without having significant difference.

Sentiment Analysis of the Palestine-Israel Crisis on Social Media using Convolutional Neural Network Delva, Dwina Sarah; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5282

The issue of Palestine and Israel is currently ongoing and is becoming increasingly heated. The struggle for territory and power is the reason for this conflict, thus attracting the world’s attention, especially that of the the Indonesians. People actively express various views in the form of opinions via social media platforms such as Twitter. Communities are competing to make posts and tweet as a form of support for either party. Various tweets appear, making it difficult to draw conclusions through manual analysis. Therefore, this study employs automatic sentiment analysis to enable mass data processing. The sentiment analysis process uses a Deep Learning algorithm, specifically the Convolutional Neural Network (CNN). Convolutional Neural Network (CNN) is a Neural Network algorithms designed for visual shape processing and developed for classification tasks. Based on the explanation provided, it is expected to provide high accuracy and achieve the designed goals. This sentiment analysis research needs to be conducted because to understand and classify various forms of public sentiment toward the issue of Palestine and Israel, thereby providing an overview of the fluctuations in public sentiment concerning this matter in Indonesia. Outcomes of this investigation found the highest performance was achieved by the Convolutional Neural Network (Oversampling) algorithm with accuracy of 93.85%, precision of 93.76%, recall of 93.95%, and F1-score of 93.86%.

Sentiment Analysis About Legislative Elections using Deep Learning with LSTM and CNN Models Angraini, Nadya Arda; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5283

The election of legislative members is a significant moment from the perspective of democracy, influencing the policies and direction of a country. In the digital era, sentiment analysis regarding the election of legislative members through social media has become increasingly important for analyzing public opinions and providing insights into how people respond to and feel about candidates, parties, or specific issues. The authors of this study employ deep learning methods, specifically Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models, for sentiment analysis related to legislative member elections. These models were developed and trained using preprocessed datasets. The aim of this research is to identify the highest accuracy values of the LSTM and CNN models and to analyze and classify public sentiment regarding the 2024 DPR member election.The results of this study indicate that deep learning methods can provide valuable insights into public sentiment during the 2024 legislative elections. Using a CNN model with a data ratio of 80:20, the proposed model can categorize and identify sentiments with the highest testing accuracy. It is clear that the data ratio, which provides an optimal balance between training and testing data, has a significant impact on model performance. As a result, the CNN model achieves the best results, with an accuracy of 93.27%, an F1 score of 93.19%, precision of 93.52%, and recall of 92.73. This research makes an important contribution by applying the CNN model, which succeeded in achieving the best results in categorizing sentiment, demonstrating the highest test accuracy in analyzing public sentiment towards the 2024 DPR member elections.

El Niño Sentiment Analysis Using Recurrent Neural Network and Convolutional Neural Network Use GloVe Putrisia, Denada; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5284

Sentiment analysis regarding the El Niño climate change is a crucial aspect in understanding public perception and response. This enables deeper identification and understanding of the sentiments evident in online conversations. Sentiment analysis through deep learning approaches using Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods is conducted. RNN is a type of artificial neural network designed to process sequential data such as text or time. On the other hand, CNN utilizes convolutional layers to scan text with filters to capture local features like phrases and keywords determining sentiment. Leveraging GloVe representation technique enables the representation of words in numerical vector form capturing semantic relationships among words, facilitating sentiment analysis related to El Niño on social media. The aim of this study is to evaluate the performance of RNN and CNN methods in classifying El Niño-related sentiment with and without GloVe word representation, and to develop a model that can provide accurate and reliable sentiment analysis results. The contribution of this research indicates that the accuracy of sentiment analysis has been improved and can be a significant reference for further research in the field of text analysis and natural language processing (NLP). This study also emphasizes the crucial role of word representation techniques like GloVe in enhancing the performance of deep learning models. The results of the study indicate that the RNN and CNN methods with the utilization of GloVe provide better sentiment classification related to the El Niño issue in social media data, showing that the use of RNN and CNN models with GloVe features perform better compared to not using GloVe features. The use of the RNN algorithm with 80:20 split ratio testing produced an accuracy score of 94.90%, recall of 94.90%, precision of 94.94%, and F1-Score of 94.85%. Meanwhile, the use of the CNN algorithm with 90:10 split ratio testing produced an accuracy score of 94.61%, recall of 93.61%, precision of 94.69%, and F1-Score of 94.58%. This results in the conclusion that sentiment analysis using RNN modeling with GloVe features has better performance than CNN modeling, with an average accuracy rate of 94.90%.

Sentiment Analysis of TikTok Shop Prohibition Using a Random Forest and Decision Tree Praja, Yudhistira Imam; Muslim L, Kemas
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5285

This research explores the impact of the closure of TikTok Shop by the Indonesian government on various aspects of the economy, the e-commerce industry, consumer behavior, and social media dynamics. As an e-commerce platform within the TikTok social media application, TikTok Shop has become a significant business information system that collects, provides, and stores information related to electronic buying and selling activities. Understanding the public's reaction to the closure of TikTok Shop is essential because it can influence consumer confidence, market stability, and future regulatory decisions. Public sentiment provides valuable insights into the potential economic and social consequences, guiding policymakers and businesses in making informed decisions. The closure of this platform has elicited both positive and negative reactions from the public, which are widely expressed through social media, especially Twitter. To analyze public sentiment regarding this issue, two relevant machine learning methods were used: Random Forest and Decision Trees. Random Forest is known for its efficiency in data mining and its ability to handle data imbalance in large datasets. Decision Trees offer similar accuracy and can be applied in both serial and parallel modes, depending on the available data capacity and memory. The results of this study are expected to provide in-depth insights into the implications of the closure of the TikTok Shop and the effectiveness of using machine learning algorithms in social sentiment analysis. This research yielded effective results with a 75.24% accuracy, 80.18% precision, 67.06% recall, and 73.04% F1 score.

Comparative Analysis of Random Forest and Convolutional Neural Network (CNN) Algorithms for Pneumonia Detection in Chest X-ray Images: Accuracy, Interpretability, and Computational Efficiency Zaena, Siffa; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7840

Pneumonia is a lung infection that can be detected through chest X-ray images. Manual diagnosis requires radiological expertise and time, thus an accurate automated method is needed. This study aims to compare the performance of two image classification algorithms, Convolutional Neural Network (CNN) and Random Forest (RF), in detecting pneumonia. The dataset used was obtained from Kaggle, consisting of 5,863 X-ray images categorized into three classes: bacterial pneumonia, viral pneumonia, and normal. Preprocessing steps include image resizing, normalization, and data augmentation. The CNN model was built using multiple convolutional and pooling layers, while RF utilized numerical features derived from histograms and texture. The CNN model demonstrated superior performance, achieving 92.4% accuracy, 93.1% precision, 91.6% recall, and 92.3% F1-score, compared to 82.7%, 80.3%, 85.1%, and 82.6% for Random Forest, respectively. Although CNN offers better accuracy, RF excels in interpretability. In conclusion, CNN is more effective for image-based pneumonia classification, yet RF remains relevant in applications requiring transparent decision-making. Potential biases, such as class imbalance and limited demographic representation in the dataset, could influence model performance and generalizability across different patient populations.

Co-Authors Abdurrahman, Azzam Abiyyu, Ahmad Syafiq Achmad Salim Aiman Adelia, Dila Adhyaksa Diffa Maulana Aditya Eka Wibowo Aditya Gifhari Soenarya Adiwijaya Aghi Wardani Agni Octavia Agus Kusnayat Ahmad Y, Rafly Ahmad Y Ahmad, Alif Faidhil Ahmad, Fathih Adawi Al Faraby, Said Alberi Meidharma Fadli Hulu Amalia Elma Sari Amien, Iqmal Lendra Faisal Andiani, Annisa Dwi Andini, Bilqiis Shahieza Angraini, Nadya Arda Anisa Herdiani Annisa Miranda Arini Rohmawati Athallah, Muhammad Rafi Aura Sukma Andini Bayu Muhammad Iqbal Bonar Panjaitan Brata Mas Pintoko Chandra Jaya Riadi Chlaudiah Julinar Soplero Lelywiary Choirulfikri, Muhammad Rizqi Damayanti, Lisyana Dana Sulitstyo Kusumo Danang Triantoro Murdiansyah David Winalda Delva, Dwina Sarah Deni Saepudin Denny Darlis Dewantara, Muhammad Pascal Dida Diah Damayanti Didit Adytia dina juni restina Dino Caesaron Donni Richasdy Donny Rhomanzah Dzidny, Dimitri Irfan Eki Rifaldi Eko Darwiyanto Ela Nadila Emrald Emrald Erwin Budi Setiawan Fakhrana Kurnia Sutrisno Farisi, Kamaludin Hanif Fatih, Muhammad Abdurrohman Al Ferdian Yulianto Fhira Nhita Guido Tamara Hadi, Salman Farisi Setya Haga Simada Ginting Haidar, Muhammad Dzakiyuddin Harahap, Rizki Nurhaliza Harmandini, Keisha Priya Haura Athaya Salka Herodion Simorangkir Hutama, Nanda Yonda Ika Puspita Dewi Intan Khairunnisa Fitriani Irgi Aditya Rachman Isman Kurniawan Jofardho Adlinnas Jondri Jondri Jordan, Brilliant Kacaribu, Isabella Vichita Kamaludin Hanif Farisi Kautsar Ramadhan Sugiharto Lukito Agung Waskito Luqman Bramantyo Rahmadi Luthfi, Muhammad Faris M. Mahfi Nurandi Karsana Mahendra Dwifebri Purbolaksono Mahendra, Muhammad Hafizh Marendra Septianta Marozi, Ericho Mehdi Mursalat Ismail Mira Rahayu Moch Arif Bijaksana Mohamad Reza Syahziar Muhammad Adzhar Amrullah Muhammad Arif Kurniawan Muhammad Yuslan Abu Bakar Muhammad Zaid Dzulfikar muhammad zaky ramadhan Muhammad Zidny Naf'an Murman Dwi Praseti Musyafaâ€™noer Sandi Pratama Nanda Yonda Hutama Naufal Furqan Hardifa Naufal Hilmiaji Naufal Rasyad Nibras Syihabil Haq Octaryo Sakti Yudha Prakasa Okky Zoellanda A. Tane Pamungkas, Danit Hafiz Praja, Yudhistira Imam Purwita, Naila Iffah Putri, Arla Sifhana Putri, Meira Reynita Putrisia, Denada R. Fajrika hadnis Putra Rafi Hafizhni Anggia Rahadian, Muhammad Rafi Ramdhani, Muhammad Rifqi Fauzi Rastim Rastim Rayhan, Muhammad Aditya Resky Nadia Rizki Luthfan Azhari Rizky Ahmad Saputra Rizky Aria Mu’allim Rizky, Fariz Muhammad Seno Adi Putra Seto Sumargo Shabrina, Ghina Annisa Siddiq, Ikhsan Maulana Sindi Fatika Sari Sri Utami Sri Widowati Sukmawan Pradika Janusange Santoso Suwaldi Mardana Syadzily , Muhammad Hasan Tri Widarmanti Try Moloharto Try Moloharto Vitalis Emanuel Setiawan Wardhani, Fitri Herinda Widi Astuti Widi Astuti Youga Pratama Yuliant Sibaroni Yusuf Nugroho Doyo Yekti Zaena, Siffa Zaenal Abidin ZK Abdurahman Baizal Zulkarnaen, Imran

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Journal : Building of Informatics, Technology and Science