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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
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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Analisis Sentimen Destinasi Wisata Kuliner Di Twitter Menggunakan Tf-idf Dan Complement Naïve Bayes Pada Dataset Tidak Seimbang Fakhrana Kurnia Sutrisno; Jondri Jondri; Kemas Muslim Lhaksmana
eProceedings of Engineering Vol 8, No 4 (2021): Agustus 2021
Publisher : eProceedings of Engineering

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Abstract

Opini masyarakat terhadap suatu destinasi wisata kuliner sangat bermanfaat bagi pemilik maupun pengunjung tempat tersebut. Maka dari itu dilakukan analisis sentimen terhadap destinasi wisata kuliner di Kota Bandung, yaitu Cuanki Serayu dan Sate DJ. Analisis sentimen diawali dengan mengambil data dari Twitter dan dilabeli secara manual menjadi positif, netral, dan negatif. Data yang sudah dilabeli dilakukan preprocessing dan oversampling pada data yang tidak seimbang. Dataset dibagi menjadi data train dan data test dengan perbandingan 70:30. Pelatihan data dilakukan menggunakan metode Complement Naïve Bayes dengan ekstraksi fitur TF-IDF. Dari hasil pengujian diperoleh nilai f1-score terbesar sebesar 0,80 dari data yang telah dilakukan oversampling. Kata kunci: analisis sentimen, oversampling, TF-IDF, Complement Naïve Bayes, f1-score
Analisis Klasifikasi Tweet Suatu Akun Film Production Dengan Kontent-based Dan Time-based Menggunakan Metode Naive Bayes Rizki Luthfan Azhari; Jondri Jondri; Kemas Muslim Lhaksmana
eProceedings of Engineering Vol 9, No 3 (2022): Juni 2022
Publisher : eProceedings of Engineering

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Abstract

AbstrakPada era digital yang serba modern ini, media sosial menjadi sarana atau platform untuk menyebarkan berbagai macam informasi secara mudah. Twitter merupakan salah satunya, twitter sendiri adalah sebuahmedia sosial yang bisa menyebarkan suatu informasi melalui tweet (kata kata yang diunggah oleh pengguna). Tweet bisa mengandung berbagai macam informasi, pembahasan, video, gambar maupun tautan ke suatu website. Suatu tweet akan disebarkan dari suatu pengguna ke pengguna lainnya dengan cara me-meretweetnya. Pada penelitian ini bertujuan untuk menganalisa apakah suatu tweet akan di retweet oleh pengguna lainnya dengan menggunakan fitur kontent-based dan time-based dengan metode klasifikasi naïve bayes serta menggunakan k-fold cross validation dengan nilai k=5 untuk melakukan splitdata. Hasil performansi yang didapatkan dengan menerapkan metode tersebut berupa nilai ratarata akurasi 61,36%, rata-rata precision yang didapatkan sebesar 65,06%, rata-rata untuk recall sebesar 55,61%, lalu rata-rata untuk f1-score sebesar 50,49%.
REVITALISASI PENAMPUNGAN DAN PENGOLAHAN SAMPAH RAMAH LINGKUNGAN ( STUDI PADA DESA CITEUREUP RW 8 KEC.BOJONGSOANG) Agus Kusnayat; Tri Widarmanti; Dino Caesaron; Kemas Muslim Lhaksmana; Murman Dwi Praseti; Denny Darlis; Dida Diah Damayanti
Prosiding COSECANT : Community Service and Engagement Seminar Vol 1, No 2 (2021)
Publisher : Universitas telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (182.344 KB) | DOI: 10.25124/cosecant.v1i2.17518

Abstract

Sampah merupakan salah satu masalah yang selalu dihadapi oleh masyarakat, melalui kegiatan pengabdian masyarakat dengan masyarakat sasar warga RW 8 Desa Citeureup Kecamatan Dayeuhkolot Kabupaten Bandung, Tim PkM yang merupakan gabungan dari 4 fakultas di Universitas Telkom, melakukan kegiatan Tahap 1 revitalisasi penampungan dan pengolahan sampah ramah lingkungan, melalui perbaikan infrastruktur sipil (perbaikan landasan mesin), sanitasi (pengaturan pembuangan air) dan kelistrikan (untuk mendukung fungsional mesin), respon masyarakat sasar terhadap kegiatan ini sangat baik 97% menyatakan kegiatan ini bermanfaat. Pengabdian kepada masyarakat tahap 1 ini merupakan awal dari optimalisasi TPS3R menuju penampungan dan pengolahan sampah yang berdaya ekonomi dengan pemanfaatan sampah organik untuk budidaya maggot, ikan lele dan tanaman hidroponik, pemanfaatan sampah anorganik melalui pemilahan sampah yang bisa dijual kembali.
Single-Label and Multi-Label Text Classification using ANN and Comparison with Naïve Bayes and SVM M. Mahfi Nurandi Karsana; Kemas Muslim L.; Widi Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6024

Abstract

Machine learning has become useful in daily life thanks to improvements in machine learning techniques. Text classification as an important part in machine learning. There are already many methods used for text classification such as Artificial Neural Network (ANN), Naïve Bayes, SVM, Decision Tree etc.  ANN is a branch in machine learning which approximate the function of natural neural network. ANN have been used extensively for classification. In this research a simple architecture of ANN is used. But it needs to be pointed out that the architecture used in this research is relatively simple compared to the cutting edge in ANN development and research to show the potential that ANN have compared to other classification method. ANN, Naïve Bayes and SVM performance are measured using f1-macro. Performance of classification model is measured of multiple single-label and multi-label dataset. This research found that in single-label classification ANN have a comparable f1-macro with 0.79 compared to 0.82 for SVM. In multi-label classification ANN have the best f1-macro with 0.48 compared to 0.44 in SVM.
Sentiment Analysis on Tweets of Kanjuruhan Tragedy Using Deep Learning IndoBERTweet Adhyaksa Diffa Maulana; Kemas Muslim Lhaksmana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6115

Abstract

The incident that occurred in Indonesian football at the Kanjuruhan Stadium was caused by unscrupulous supporters who entered the field and unscrupulous officers who fired tear gas into the stands. With this incident, many responses and opinions were given by the Indonesian people through social media Twitter in the form of positive, negative, and neutral opinions. This difference in opinion occurred because of the many victims who died or were injured, with many supporters who did not like the actions taken by the authorities during the riots. With this incident, the government must make decisions to ease the concerns of the community. Therefore, research will be conducted to analyze the sentiment of public opinion regarding the Kanjuruhan tragedy using the IndoBERTweet method with a comparison using naive Bayes. The results of this study using the IndoBERTweet method get better results than naive Bayes method. With the results of the IndoBERTweet method 88% accuracy, 82% precision value, 85% recall value, and 84% f1-score value, naive the Naive Bayes results are 62% accuracy, 59% Precision Value, 61% Recall Value, and f1-Score of 59%.
Retweet Prediction Based on User-Based, Content-Based, and Time-Based Features Using ANN Optimized with GWO Irgi Aditya Rachman; Jondri Jondri; Kemas Muslim L
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1067

Abstract

Social media has emerged as immensely popular and favored platforms among the masses today. Twitter, being one of the most renowned social media platforms, allows users to express themselves through tweet postings. Retweeting is a crucial feature on Twitter, enabling users to disseminate tweets authored by others. In this context, this research aims to predict retweet behavior using User-Based, Content-Based, and Time-Based features, coupled with an Artificial Neural Network classifier optimized with Grey Wolf Optimization. One of the challenges in retweet prediction lies in class imbalance, where the number of retweets on certain tweets is significantly disproportionate compared to others. To address this issue, this study implements undersampling and oversampling techniques. Undersampling reduces the number of samples from the majority class, whereas oversampling involves duplicating or synthesizing samples from the minority class, thereby creating class balance. The research successfully achieves promising results in retweet prediction. After applying oversampling techniques, the classification process attains an accuracy of 85.58%, precision of 87.77%, recall of 83.92%, and F1-score of 85.80%. These results demonstrate the effectiveness of the proposed method in retweet prediction and handling class imbalance issues
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

Abstract

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%.
Analisis Teks Pelamar Untuk Klasifikasi Kepribadian Menggunakan Multinomial Naïve Bayes dan Decision Tree Nanda Yonda Hutama; Kemas Muslim Lhaksmana; Isman Kurniawan
JURNAL INFOTEL Vol 12 No 3 (2020): August 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i3.505

Abstract

Employees' qualities affect companies' performances and with a large number of applicants, it's difficult to find suitable applicants. To help with it, companies carry out psychological tests to know applicants' personalities, since personality's considered to have a relationship with work performances. But psychological testing requires a lot of effort, cost, and human resources. Thus with a system that can classify personalities through text can help reduce the effort needed. Similar studies carried out with the big five personalities as the theoretical basis and used one of the personality traits, namely using the k-NN method with 65% accuracy. Based on these studies, accuracy can improve by finding the best parameters using all of the big five personalities. This research is conducted based on the big five personality traits and related traits, namely consciousness and agreeableness. The data used is text data that's been labelled, pre-processed and feature selected. The clean text data is used to create a classification model using multinomial Naive Bayes and decision trees. There are 6 models built based on 3 work cultures, decision tree with an accuracy of 33%, 66%, 80%, and multinomial naïve Bayes with an accuracy of 83%, 50%, 60%, which resulted as better performance.
2024 Presidential Election Sentiment Analysis in News Media Using Support Vector Machine Bayu Muhammad Iqbal; Kemas Muslim Lhaksmana; Erwin Budi Setiawan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 2 (2023): Februari 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i2.3051

Abstract

The 2024 presidential election is an event for all Indonesian people to determine their best leader. The presidential and vice presidential candidates are also competing to give their best efforts so that they can be elected as President and Vice President. The news media also provide news related to the 2024 presidential election with various titles that can interest their readers. Not infrequently the titles raised contain words that have sentiments, both positive and negative. In order to facilitate the analysis of the sentiments of these news titles, it is necessary to build a system that can detect the sentiments of these titles. In this study, we built a sentiment analysis system using the Support Vector Machine (SVM) method on news headline data obtained from online news media to detect whether news headlines contain positive or negative sentiments. For feature exctraction we compare the effect of FastText word embedding with TF-IDF for feature extraction. In the SVM method, several experiments were carried out on the attributes of C, kernel, gamma, and the ratio of the test data. The results obtained are a FastText can outperform TF-IDF for feature extraction. Also, combination of Kernel, C, and gamma values that give the best accuracy score of rbf, 1, and auto respectively at a test data ratio of 90:10, with an accuracy score of 99%.
Retweet Prediction Using ANN Method and Artificial Bee Colony Jondri Jondri; Kamaludin Hanif Farisi; Kemas Muslim Lhaksmana
Computer Science Research and Its Development Journal Vol. 15 No. 2: June 2023
Publisher : LPPM Universitas Potensi Utama

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Abstract

In the ongoing modern era, the rapid dissemination of information takes place, utilizing various channels for data exchange. One such platform is the social media platform Twitter, renowned for its swift and extensive information propagation. A pivotal factor contributing to information distribution on Twitter is the retweet feature, whereby users can redistribute content to their audience. A study has been conducted to forecast this retweet activity by employing the Artificial Neural Network classification method in conjunction with the Artificial Bee Colony optimization approach. This study leverages diverse features, encompassing content-based feature, user-based feature, and time-based feature. The evaluation results from this study reveal that the proposed method achieves an accuracy value of around 83% with the highest accuracy value reaching 84%. These findings indicate that the fusion of the Artificial Neural Network classification method executed with optimization using the Artificial Bee Colony algorithm yields dependable and consistent performance in predicting retweet activities.
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 Musyafa’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 Razaka, Akmal Sidki 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