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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 Menggunakan Metode Naive Bayes dan Support Vector Machine pada Ulasan Aplikasi Spotify Ramdhani, Muhammad Rifqi Fauzi; Lhaksmana, Kemas Muslim
eProceedings of Engineering Vol. 10 No. 3 (2023): Juni 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak-Pergeseran kebiasaan memutar lagu secara digital didukung oleh kemudahan akses yang tersedia di berbagai perangkat, membuat pengguna bisa mendegarkan lagu kapanpun dan dimanapun waktunya. Spotify merupakan platform nomor satu sebagai penyedia jasa musik dan audio gratis dengan hampir 422 juta pengguna aktif dan menguasai 31% pangsa pasar skala global. Dengan banyaknya unduhan yang sudah mencapai satu juta kali, Spotify mendapatkan nilai rating 4.4 dan ulasan oleh para penggunanya. Pengguna diberikan kebebasan untuk mengekspresikan hasil kepuasaan, kritik, dan saran terhadap aplikasi. Ulasan tersebut bisa digunakan sebagai umpan balik untuk perusaahan dalam meningkatkan layanan dan mengembangkan inovasi selanjutnya. Analisis sentimen diperlukan untuk mengolah ulasan menjadi informasi yang bermanfaat dengan melalui beberapa tahapan pembersihan data terlebih dulu. Pembobotan menggunakan TF-IDF dilakukan sebelum masuk kedalam proses klasifikasi menggunakan Naive Bayes dan Support Vector Machine. Nilai F1-Score terbaik didapatkan pada metode SVM kernel RBF dengan nilai C & gamma optimum menghasilkan nilai F1-Score tertinggi sebesar 81% pada dataset ulasan aplikasi Spotify di layanan GooglePlay Store.Kata kunci-naive bayes, support vector machine, spotify, analisis sentimen, ulasan
Teknik Recommender System Menu Makanan dengan Pendekatan Contextual Model dan Multi-Criteria Decision Making pada Orang Dewasa Kacaribu, Isabella Vichita; Setiawan, Erwin Budi; Lhaksmana, Kemas Muslim
eProceedings of Engineering Vol. 10 No. 4 (2023): Agustus 2023
Publisher : eProceedings of Engineering

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Abstract

Wisata Kuliner adalah kegiatan yang popular pada saat ini. Banyak tempat makan yang menawarkan makanan-makanan dengan tampilan yang menarik, murah, atau enak. Beberapa masyarakat mendapatkan informasi mengenai wisata kuliner atau daftar makanan melalui media sosial, berita maupun melalui media cetak. Sehingga banyak dari mereka menentukan menu makanan yang mereka santap melalui media sosial. Banyak kriteria yang digunakan dalam memilih makanan, seperti ada yang melihat kandungan kalorinya, harganya, lokasinya, atau yang lainnya. Seiring berkembangnya teknologi informasi, sistem rekomendasi telah semakin dibutuhkan oleh masyarakat untuk membantu pengguna dalam mendapatkan informasi menu makanan yang relavan. Ada metode untuk merekomendasikan makanan berdasarkan contextual model dan multi-criteria decision yang dapat membantu pengguna memilih makanan yang cocok. Berdasarkan pada metode Weighted Sum Model, penelitian ini ingin membuat suatu teknik yang lebih baik dengan menggunakan terapan Contextual Model. Contextual Model membuat pengguna menjadi lebih mengerti dalam penggunaan sistem dan mudah dimengerti.Kata kunci— wisata kuliner, recommender system, contextual model, multi-criteria decision, weighted sum model.
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
Detecting Language Anxiety in Indonesian Students: Deep Learning and Traditional Classification Methods for English Learning Anxiety Lhaksmana, Kemas Muslim; Falif , Muhammad Sya’bani; Nurhayati, Iis Kurnia; Rezaldi, Muhammad Yudhi; Prakasa, Esa; Roedavan, Rickman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7109

Abstract

Mastery of the English language represents a fundamental determinant of professional achievement, particularly for individuals seeking to develop their careers and participate in international contexts. However, when learning a foreign language such as English, Indonesian students may experience language anxiety that cause them to hesitate to practicing English in the class, both in orally and in writing. For English teachers, it is crucial to identify students experiencing language anxiety early on, so that they may provide appropriate teaching strategies and interventions from the first class meeting. To address this issue, this study compares machine learning methods to provide a solution for early detection of students experiencing language anxiety. Furthermore, these methods are classification models, including LSTM, GRU, decision tree, naïve Bayes, logistic regression, and SVM. The implementation of each of these models is combined with different text representation techniques, such as Word2Vec, BERT, FastText, Glove, and TF-IDF. The advantage of our model is that despite the imbalance, limited, and smaller than baseline dataset size, this research finds that GRU with focal loss achieves the highest F1 score of 0.89. This result outperforms our baseline and thus suggests that this method is effective in detecting students who experience language anxiety.
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 Esa Prakasa, Esa Fakhrana Kurnia Sutrisno Falif , Muhammad Sya’bani Farisi, Kamaludin Hanif Fatih, Muhammad Abdurrohman Al Ferdian Yulianto Fhira Nhita Ghina Annisa Shabrina 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 Iis Kurnia Nurhayati 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 Yudhi Rezaldi, Muhammad Yudhi 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 Roedavan, Rickman Seno Adi Putra Seto Sumargo 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