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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 Jurnal Abdimas Kartika Wijayakusuma Prosiding Konferensi Nasional PKM-CSR Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Indonesia Sosial Teknologi eProceedings of Engineering Dike Jurnal INFOTEL CSRID LOGIC: Jurnal Penelitian Informatika IJoICT (International Journal on Information and Communication Technology)
Kemas Muslim Lhaksmana
Telkom University
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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Klasifikasi Ayat Al-Quran Terjemahan Bahasa Inggris Menggunakan Long Short Term Memory dan Bidirectional Long Short Term Memory Arif Irfan, Rafisa; Muslim Lhaksmana, Kemas
eProceedings of Engineering Vol. 10 No. 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak— Di dalam Al-Quran terdapat kandungan ayat yang berbeda-beda, maka sangatlah penting untuk memahami ayat Al-Quran. Al-Quran terdiri atas 30 juz, 144 surat, 6236 ayat, dan 77845 kata. Banyak ayat dan kata yang terdapat pada Al-Quran, untuk mempermudah umat muslim dalam mempelajari ayat maka perlu dilakukan pengklasifikasian terhadap ayat Al-Quran. Pada penelitian ini dilakukan klasifikasi multi label ayat Al-Quran berdasarkan topik-topik yang ada. Perancangan sistem dilakukan dengan menggunakan dua metode yaitu convolutional long short term memory (C-LSTM) dan bidirectional long short term memory (Bi-LSTM) yang mampu mengklasifikasikan ayat kedalam kelompoknya masing-masing. C-LSTM mampu mengungguli Bi-LSTM pada hampir setiap skenario. Nilai hamming loss terbaik yang diberikan C-LSTM sebesar 0.09985, dan BiLSTM 0.10122 pada skenario 90% data latih dan dropout.Kata Kunci— Klasifikasi, Multi Label, Recurrent Neural Network, Long Short Term Memory, Convolutional Neural Network, Bidirectional Long Short Term Memory, Hamming loss.
Klasifikasi Komentar Toxic Pada Sosial Media Menggunakan SVM, Information Gain dan TF-IDF Ilham Maulana, Muhammad; Muslim Lhaksmana, Kemas; Dwifebri, Mahendra
eProceedings of Engineering Vol. 10 No. 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak — Sosial media merupakan suatu bentuk perantara interaksi sosial secara online. Aplikasi media sosial pun sudah dalam banyak bentuk dan di dalam sosial media ini meskipun banyak hal positif yang dapat diambil, ada beberapa juga halhal negatif contoh nya toxic comment. Toxic comment sendiri tidaklah mudah untuk dideteksi secara manual, maka penelitian berencana untuk mengklasifikasikan toxic comment tersebut menggunakan machine learning. Beberapa penelitian untuk klasifikasi toxic comment sudah dilakukan, dalam beberapa penelitian tersebut digunakan metode Support Vector Machine. Dalam penelitian ini metode yang digunakan adalah Support Vector Machine (SVM) sebagai classifier, Information Gain sebagai feature selection dan TF- IDF sebagai feature extraction. Data-data yang dikumpulkan adalah melalui cuitan twitter beberapa pengguna di media sosial tersebut. Komentarkomentar tersebut dikumpulkan menjadi satu lalu diklasifikasikan menggunakan metode-metode yang sudah disebutkan.Kata kunci— Sosial media, Klasifikasi teks, Toxic comment, SVM
Perbandingan Algoritma Cnn Dan Svm Untuk Analisis Sentimen Mengenai Kenaikan Harga Bahan Bakar Minyak Ahmad Y, Rafly Ahmad Y; Muslim L, Kemas
eProceedings of Engineering Vol. 10 No. 6 (2023): Desember 2023
Publisher : eProceedings of Engineering

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Abstract

Opini-opini maupun keluhan masyarakat yang disampaikan melalui tweet dapat diolah untuk mengetahui sentimen yang ada di dalam tweet tersebut. Pada penelitian ini dilakukan analisis sentimen menggunakan machine learning. Penggunaan machine learning ini dapat mempermudah saat pengambilan data dan pemrosesan data, yang tidak memerlukan banyak waktu dan biaya. Proses klasifikasi data tweet yang dilakukan dalam penelitian ini yaitu data yang mengandung sentimen positif dan sentimen negatif mengenai kebijakan pemerintah yaitu kenaikan harga bahan bakar minyak (BBM). Metode klasifikasi yang digunakan untuk penelitian ini menggunakan Convolutional Neural Network (CNN) dan Support Vector Machine (SVM). Untuk pengambilan data tweet menggunakan metode crawling. Hasil yang didapatkan dari penelitian dengan melakukan evaluasi menggunakan Confusion Matrix mendapatkan bahwa algoritma SVM mendapatkan nilai akurasi yang cukup tinggi sebesar 85% dengan menggunakan max features 510 dan rasio 80:20 dibandingkan dengan algoritma CNN yang memiliki nilai akurasi tertingginya di angka 74% menggunakan nilai max features 300 dan rasio 80:20. Untuk nilai penggunaan cross fold validation CNN mendapatkan nilai rata-rata akurasi tertingginya 78% dengan k=10 sedangkan SVM 87%.Kata Kunci: analisis sentimen, pembelajaran mesin, CNN, SVM, twitter, sosial media
Klasifikasi Multi-Label Ayat-Ayat Al-Qur’an Menggunakan Random Forest dan Word Centrality Rizky Aria Mu’allim; Kemas Muslim Lhaksmana
LOGIC: Jurnal Penelitian Informatika Vol. 2 No. 2 (2024): Desember 2024
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/logic.v2i2.8808

Abstract

Abstrak Penelitian ini memanfaatkan teknologi untuk analisis otomatis topik dalam ayat Al-Qur’an, mengembangkan cakupan analisis dengan klasifikasi ke dalam 15 kategori, termasuk satu ’tidak berlabel’. Fokus penelitian meliputi perbandinganefektivitas antara Random Forest, SVM, dan Na¨ıve Bayes dalam sistem klasifikasi topik ayat Al-Qur’an, dengan Word Centrality sebagai fitur. Tahapan pra-pemrosesan seperti tokenisasi dan penghapusan stopword diterapkan, bersama denganmetode TF-IDF dan TW-IDF. Hasil menunjukkan bahwa Random Forest mencatat skor Hamming Loss terendah dalamskenario TW-IDF, namun hasil TFIDF dalam skenario menggunakan stopword tidak lebih baik dibandingkan dengan SVM,berturut-turut adalah 0.949 dan 0.0927. Pengujian tanpa penghapusan stopword juga menunjukkan keunggulan relatif hasilhamming loss Random Forest dalam beberapa skenario. Hasil penelitian ini mengindikasikan bahwa penerapan word centrality sebagai metode ekstraksi fitur dalam klasifikasi ayat-ayat Al-Qur’an berpengaruh pada penurunan nilai HammingLoss.
Sentiment Analysis of Digitalization of Small and Medium Enterprise on Social Media X Using SVM and KNN Methods Haidar, Muhammad Dzakiyuddin; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6723

Abstract

The rapid digitalization of Small and Medium Enterprises (SMEs) has led to significant shifts in business operations, especially in their adaptation to digital platforms. Public perception towards this digital transformation is crucial to understand, as it reflects the success and acceptance of these efforts. This research conducts sentiment analysis on social media platform X to classify public opinions regarding the digitalization of SMEs. The analysis employs two machine learning algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. The study compares the performance of both models under baseline and hyperparameter-tuned conditions. The results show that the SVM model consistently outperforms KNN in terms of accuracy, precision, recall, and F1-score. The highest accuracy achieved by the SVM model is 81.97% after hyperparameter tuning with a sigmoid kernel. Meanwhile, the best KNN model records an accuracy of 81.31% using Manhattan distance with 11 neighbors. This study demonstrates that SVM provides better stability and performance in sentiment classification related to SME digitalization. The findings are expected to help policymakers better understand public sentiment and formulate more effective strategies for supporting SME digital transformation.
Collaboration between Convolutional Neural Network and Semantic Search for English Hadith Search Using Automatic Topic Classification, TF-IDF, and Sentence-BERT Razaka, Akmal Sidki; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8861

Abstract

This research was conducted with the intention of developing an English-language hadith search system that is not only syntactically accurate, but also contextually appropriate. The system was developed using a combination of convolutional neural networks (CNN) and two text representation methods, namely Term Frequency–Inverse Document Frequency (TF-IDF) and Sentence-BERT (SBERT). CNN is used to classify hadiths into seven main categories based on chapter titles. In the semantic retrieval stage, TF-IDF and SBERT were utilized to represent the text of the hadith and user queries, then both were evaluated using cosine similarity. Testing was conducted using five queries commonly used in Islamic studies, then evaluated manually for semantic similarity. As a result, the tuned CNN achieved a classification accuracy of 94%. On the other hand, although the TF-IDF approach produced greater similarity results, SBERT proved to be superior in generating more relevant results in semantic searches. These results indicate that TF-IDF is superior in terms of speed, but SBERT is better at understanding sentence context in depth. This research contributes to the development of a meaning-based hadith search system and emphasizes the importance of a semantic approach in religious text search. Moving forward, system development can be directed toward multilingual support and evaluation on a larger scale.
Multi-Label Topic Classification on the Qur'an using the K-Nearest Neighbor and Latent Semantic Analysis Methods Ghina Annisa Shabrina; Kemas Muslim Lhaksmana
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 12 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i12.1340

Abstract

The Qur'an, comprising over 80,000 words, 6,236 verses, and 114 surahs, presents a multifaceted and deeply significant text that demands a nuanced understanding of historical context, classical Arabic, and exegesis. To analyze and classify its content, various methodologies have been employed, including K-Nearest Neighbor (KNN) and Latent Semantic Analysis (LSA). This research investigates the effectiveness of combining KNN with LSA for multi-label topic classification of Qur'anic verses. The study reveals that KNN alone achieved a micro average F1-score of 0.49, demonstrating reliable performance particularly for topics such as "aqidah" (creed) and "worldly matters." When LSA was applied with 100 components, there was a decrease in performance, reflected by a drop in the micro average F1-score to 0.43 and an increase in Hamming loss to 0.1657. However, as the number of LSA components increased to 200 and 300, performance improved, with micro average F1-scores rising to 0.45 and 0.47, and Hamming loss values decreasing to 0.1507 and 0.1466, respectively. This indicates that while LSA can enhance KNN performance, optimal results are achieved with a higher number of components
Pengembangan Website Desa Wisata Berbasis Partisipasi Masyarakat untuk Penguatan Promosi dan Ekonomi Lokal: Studi Kasus Desa Banjarsari Selviandro, Nungki; Ramadhan, Nur Ghaniaviyanto; Lhaksmana, Kemas Muslim; Erfianto, Bayu
Jurnal Abdimas Kartika Wijayakusuma Vol 7 No 1 (2026): Jurnal Abdimas Kartika Wijayakusuma
Publisher : LPPM Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26874/jakw.v7i1.1283

Abstract

Pengembangan desa wisata berbasis digital menjadi strategi penting dalam memperkuat promosi destinasi dan mendorong penguatan ekonomi lokal. Desa Wisata Banjarsari memiliki potensi wisata alam dan budaya yang beragam, namun pemanfaatan teknologi digital sebagai media promosi masih belum optimal. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengembangkan website desa wisata berbasis partisipasi masyarakat guna meningkatkan visibilitas destinasi wisata serta mendukung pemberdayaan ekonomi lokal. Metode yang digunakan adalah pendekatan partisipatif melalui Participatory Action Research (PAR) yang dikombinasikan dengan prinsip Community-Based Tourism (CBT) dan pemberdayaan digital. Kegiatan dilaksanakan melalui tahapan pemetaan potensi, perencanaan partisipatif, pelatihan dan pendampingan pengelolaan konten digital, serta monitoring dan evaluasi. Hasil kegiatan menunjukkan bahwa masyarakat, khususnya pengelola BUMDes, pemuda desa, dan pelaku UMKM, mampu terlibat aktif dalam pengelolaan website desa wisata. Website yang dikembangkan berfungsi sebagai pusat informasi wisata dan etalase digital produk lokal. Kegiatan ini berkontribusi terhadap peningkatan kapasitas masyarakat dalam pengelolaan promosi digital serta membuka peluang penguatan ekonomi lokal berbasis pariwisata.
QURANIC KNOWLEDGE GRAPH: A MULTIDISCIPLINARY APPROACH TO MAPPING SEMANTIC NETWORKS IN THE QURAN Kemas Saleh Rahmat Wiharja; Muhammad Arif Bijaksana; Kemas Muslim Lhaksmana; Hafizh Putra Ardhana; Muhammad Aqil Ghazali Anhein
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

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Abstract

Understanding the Al-Quran is the eternal goal of every Muslim, wherever they are. Before the emergence of the Knowledge Graph, readers of the Al-Quran needed to move from the Al-Quran to Tafsir, Hadith, scholar opinions or other resources in order to grasp the full meaning of a verse or a chapter in the Al-Quran. To help the readers of the Al- Quran in pondering the meaning of the Al-Quran and linking the verses to the meaning of the word or the interpretation of the verse in a tafsir book, we propose the first multi-layer Quranic Knowledge Graph that uses the property graph format. We also add a chatbot on top of our Quranic Knowledge Graph. And to save cost in using a Large Language Model, we deploy a vector database to memorize the previously answered user queries and their corresponding Cypher translations. We evaluate our approach using three point of views: large language model, knowledge graph, and chatbot. The result of evaluation shows that our Quranic Knowledge Graph achieves 100% correctness and 90% accuracy, despite only covering 9 parts of the Al-Quran. For the interested readers, please access https://zentilax.github.io/quranic-chatbot-UI/ for exploring the quranic knowledge graph
Symptom-Based Classification of Migraine Severity Using Random Forest and LassoNet Syehan Fariz Gustomo; Kemas Muslim Lhaksmana
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9900

Abstract

This study aims to develop and compare classification models for predicting symptom-based migraine intensity using a public dataset from Kaggle. The research was conducted on multiclass data with an imbalanced class distribution. Therefore, target creation and result interpretation must be carefully designed so that the resulting evaluation remains consistent with the characteristics of the data used. In this study, the Intensity variable was recoded into three operational classes: low, moderate, and high. The features used included symptoms, characteristics of migraine episodes, and `symptom_count`, which represents the number of symptoms in each sample. The two models compared were Random Forest and LassoNet, both of which were tested using Stratified 5-Fold Cross-Validation. Model performance was assessed using the Macro F1-score as the primary metric, Balanced Accuracy as the main supplementary metric, and Accuracy as a complementary metric. The test results showed that Random Forest performed better, with a Macro F1-score of 0.6366, Balanced Accuracy of 0.6181, and Accuracy of 0.6225. Meanwhile, LassoNet achieved a Macro F1-score of 0.2474, a Balanced Accuracy of 0.3333, and an Accuracy of 0.5900. These results indicate that symptom patterns in the dataset can still be utilized to distinguish migraine intensity within a computational classification framework, although the separation between closely related classes is not yet fully robust.
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, Fathih Adawi Al Faraby, Said Alberi Meidharma Fadli Hulu Alif Faidhil Ahmad Amalia Elma Sari Amien, Iqmal Lendra Faisal Andiani, Annisa Dwi Andini, Bilqiis Shahieza Angraini, Nadya Arda Anisa Herdiani Annisa Miranda Arif Irfan, Rafisa Arini Rohmawati Aura Sukma Andini Azzam Abdurrahman Bayu Erfianto Bayu Muhammad Iqbal Bonar Panjaitan Bramandyo Widyarto, Edgarsa 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 Dwifebri, Mahendra Dzidny, Dimitri Irfan Eki Rifaldi Eko Darwiyanto Ela Nadila Emrald Emrald Erwin Budi Setiawan Esa Prakasa Fakhrana Kurnia Sutrisno Farisi, Kamaludin Hanif Fatih, Muhammad Abdurrohman Al Ferdian Yulianto Fhira Nhita Ghina Annisa Shabrina Guido Tamara Hadi, Salman Farisi Setya Hafizh Putra Ardhana Haga Simada Ginting Haidar, Muhammad Dzakiyuddin Harahap, Rizki Nurhaliza Harmandini, Keisha Priya Haura Athaya Salka Herodion Simorangkir Hutama, Nanda Yonda Iis Kurnia Nurhayati Ika Puspita Dewi Ilham Maulana, Muhammad Intan Khairunnisa Fitriani Irgi Aditya Rachman Isman Kurniawan Jofardho Adlinnas Jondri Jondri Jordan, Brilliant Kacaribu, Isabella Vichita Kamaludin Hanif Farisi Kautsar Ramadhan Sugiharto Kemas Saleh Rahmat Wiharja 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 Aqil Ghazali Anhein Muhammad Arif Bijaksana Muhammad Arif Kurniawan Muhammad Rafi Athallah Muhammad Sya’bani Falif Muhammad Yudhi Rezaldi 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 Nungki Selviandro Nur Ghaniaviyanto Ramadhan 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 Rickman Roedavan Rizki Luthfan Azhari Rizky Ahmad Saputra Rizky Aria Mu’allim Rizky, Fariz Muhammad Seno Adi Putra Seto Sumargo Siddiq, Ikhsan Maulana Sindi Fatika Sari Sri Utami Sri Widowati Sukmawan Pradika Janusange Santoso Suwaldi Mardana Syadzily , Muhammad Hasan Syehan Fariz Gustomo 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