Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

17.862

P-Index

This Author published in this journals

All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Dinamisia: Jurnal Pengabdian Kepada Masyarakat Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Linguistik Komputasional Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Jurnal Pengabdian Masyarakat Indonesia Journal La Multiapp eProceedings of Engineering Eduvest - Journal of Universal Studies Jurnal INFOTEL

Yuliant Sibaroni

Telkom University

Author-ID : 3730136

Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

Published : 153 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3 4 5

The Optimal High Performance Computing Infrastructure for Solving High Complexity Problem Yuliant Sibaroni; Fitriyani Fitriyani; Fhira Nhita
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.3586

The high complexity of the problems today requires increasingly powerful hardware performance. Corresponding economic laws, the more reliable the performance of the hardware, it will be comparable to the higher price. Associated with the high-performance computing (HPC) infrastructures, there are three hardware architecture that can be used, i.e. Computer Cluster, Graphical Processing Unit (GPU), and Super Computer. The goal of this research is to determine the most optimal of HPC infrastructure to solve high complexity problem. For this reason, we chose Travelling Salesman Problem (TSP) as a case study and Genetic Algorithm as a method to solve TSP. Travelling Salesman Problem is belong often the case in real life and has a high computational complexity. While the Genetic Algorithm (GA) is belong a reliable algorithm to solve complex cases, but has the disadvantage that the time complexity level is very high. In some research related to HPC infrastructure comparison, the performance of multi-core CPU single node for data computation has not been done. Whereas the current development trend leads to the development of PCs with higher specifications like this. Based on the experiments results, we conclude that the use of GA is very effective to solve TSP. the use of multi-core single-node in parallel for solving high complexity problems as far as this is still better than the two other infrastructure but slightly below compare to multi-core single-node serially, while GPU deliver the worst performance compared to others infrastructure. The utilization of a super computer PC for data computation is still quite promising considering the ease of implementation, while the GPU utilization for the purposes of data computing is profitable if we only utilize GPU to support CPU for data computing.

Aplikasi Pelayanan Administrasi Penduduk Desa Berbasis Web Programing Yuliant Sibaroni; Erwin Budi Setiawan; Mahmud Imrona; Feby Ali Dzuhri
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2015
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Permasalahan yang dihadapi olehinstansi pemerintahan dalam lingkup desa salahsatunya adalah proses pembuatan surat menyuratyang masih manual, dimana hal ini berdampakterhadap pelayanan yang kurang maksimal terhadappenduduknya. Penggunaan aplikasi microsoft officedalam pembuatan surat memiliki beberapakelemahan utama seperti ketergantungan terhadapkemampuan aparatur desa yang tinggi yangberakibat format surat menjadi berubah-ubah sertarawan terhadap keakuratan pencatatan data suratyang telah dibuat. Disisi lain, umumnya kemampuanIT aparatur desa adalah dibawah rata-rata dibandingtenaga administrasi lainnya sehingga penggunaanaplikasi khusus pelayanan administrasi persuratanmenjadi mutlak dibutuhkan. Adanya aplikasi suratmenyurat desa berbasis web programing inidiharapkan dapat menyelesaikan permasalahan yangsedang dihadapi oleh setiap desa dalam melakukanpelayanan administrasi persuratan yang lebih baik.Manfaat lainnya adalah dapat membantu dalampencatatan data persuratan yang ada sehingga akanmembantu desa untuk melihat potensi desa secaralebih jelas, pelayanan administrasi yang dirasakanmasyarakat menjadi lebih baik dan transparan sertapraktek-praktek KKN dalam pembuatan surat didesa menjadi berkurang

Effectiveness of SVM Method by Naïve Bayes Weighting in Movie Review Classification Fadli Fauzi Zain; Yuliant Sibaroni
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.7770

Classification of movie review belongs to the realm of text classification, especially in the field of sentiment analysis. One familiar text classification method used is support vector maching (SVM) and Naïve Bayes. Both of these methods are known to have good performance in handling text classification separately. Combining these two methods is expected to improve the performance of classifier compared to working separately. This paper reports the effort to classify movie reviews using the combined method of Naïve Bayes and SVM with Naïve Bayes as weights. This combined method is commonly called NBSVM. The results showed the best accuracy is obtained if the classification is done by the NBSVM method, which is equal to 88.8% with the combined features of unigram and bigram and using pre-processing in the form of data cleansing only.

Kategorisasi Berita Menggunakan Metode Pembobotan TF.ABS dan TF.CHI Muhammad Arif Kurniawan; Yuliant Sibaroni; Kemas L Muslim
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 2 (2018): September, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.2.236

Dengan kemajuan teknologi saat ini, berita dapat ditemukan dengan mudah dan berjumlah sangat banyak dalam bentuk digital yang menyebabkan diperlukannya suatu teknik untuk mengkategorikan berita-berita tersebut ke dalam topik tertentu agar mempermudah pembaca menemukan berita sesuai dengan topik yang diinginkan. Kategorisasi teks merupakan suatu teknik yang dapat mengkategorikan berita ke dalam topik yang telah ditentukan secara otomatis. Salah satu proses yang penting dalam kategorisasi adalah ekstraksi fitur yang mana unigram binary merupakan salah satu ekstraksi fitur yang dasar dibandingkan dengan term weighting yang dalam penelitian ini akan menggunakan metode pembobotan TF.ABS dan TF.CHI untuk memperoleh hasil kategorisasi berita yang optimal. Berdasarkan hasil pengujian, rata-rata akurasi yang didapatkan dari tiga sumber data pada ekstraksi fitur unigram binary sebesar 90.44%. Sedangkan pada metode pembobotan TF.ABS sebesar 95.74% dan TF.CHI sebesar 95.87%. Berdasarkan hasil akurasi tersebut, dapat disimpulkan bahwa term weighting lebih baik dibandingkan dengan unigram binary. Metode pembobotan TF.ABS dan TF.CHI sama-sama baik dalam kategorisasi karena tidak berbeda secara signifikan dalam performansinya. Pada hasil pengujian lainnya menunjukkan bahwa proses stemming tidak memberikan banyak pengaruh terhadap akurasi kategorisasi berita, namun proses ini dapat mengefisiensikan waktu hingga 45%.

Deteksi Kemiripan Dokumen Bahasa Indonesia Menggunakan Algoritma Smith-Waterman dan Algoritma Nazief & Andriani Bunga Sari; Yuliant Sibaroni
Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 3 (2019): December, 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2019.4.3.365

Perkembangan teknologi semakin canggih dengan adanya internet. Internet yang dapat dengan mudah diakses untuk mencari informasi dan dokumen dapat memicu adanya tindak plagiarisme. Setiap dokumen yang melakukan tindakan plagiarisme akan susah dikenali tanpa adanya sistem yang dapat mengenali kesamaan dokumen. Sistem yang dapat mendeteksi plagiarisme dengan mencari kemiripan pada dokumen dibutuhkan. Dalam penelitian ini digunakan algoritma Nazief & Andrianidalam proses penentuan kemiripan pada dokumen teks dan algoritma Smith-Watermanuntuk mengidentifikasi kesamaan yang paling signifikan (local alignment) dari dua buah rangkaian sekuens string. Hasil akhir yang didapatkan perbandingan dua sequence dengan bantuanpreprocessingmemiliki tingkat perhitungansimilarity yang lebih besar dalam mendeteksi kemiripan dokumen. Kata Kunci: Dokumen, Plagiarisme, Algoritma Smith-Waterman, Algoritma Nazief & Andriani

Klasifikasi Sentimen Ulasan Tempat Makan Berbahasa Indonesia dengan Lexicon dan Improved Naive Bayes Agi Maulana; Yuliant Sibaroni
Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 3 (2019): December, 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2019.4.3.369

Ulasan tempat makan pada situs daring seringkali memberikan skor yang tidak sesuai dengan makna pada ulasan. Ulasan dapat berskor rendah namun ulasan mengandung makna positif dan ulasan berskor tinggi dapat mengandung makna yang negatif. Berbagai upaya klasifikasi sentimen ulasan dengan menggunakan analisis sentimen telah dilakukan pada banyak penelitian. Namun analisis sentimen dengan hanya mengandalkan pendekatan supervised learning memberikan hasil salah satu kelas cenderung lebih sering muncul sehingga berakibat pada menurunnya kinerja pengklasifikasi. Dalam makalah ini, pendekatan Improved Naive Bayes yaitu Naive Bayes dengan fitur unigram dan bigram dipadukan dengan pendekatan menggunakan lexicon diusulkan untuk meningkatkan kinerja pengklasifikasi. Fitur diperoleh dengan mengekstrak pola POS TAG yang mengandung kata atau frasa yang mengekspresikan emosi yang relevan dengan ulasan tempat makan. Lexicon dibangun secara manual dengan mengumpulkan kata dan frasa unigram dan bigram yang menunjukan emosi yang relevan diungkapkan pada ulasan tempat makan. Pengklasifikasi dengan menggunakan Improved Naive Bayes menunjuakan kinerja yang lebih baik dibandingkan pengklasifikasi menggunakan Naive Bayes. Improved Naive Bayes memperoleh skor precision 80%, recall 77%, dan F1 76%. Sedangkan Naive Bayes memperoleh skor precision 68%, recall 60%, dan F1 56%.

Prediksi Tingkat kerawanan penyakit Demam Berdarah Menggunakan Algoritma K-NN dan Random forest (studi kasus di Bandung) Abduh Salam; Sri Suryani Prasetiyowati; Yuliant Sibaroni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 3 (2020): Juni 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Indonesia is a country that is prone to Dengue Fever, this happens because Indonesia is a country with a tropical climate. More than 50 years after Indonesia contracted the dengue virus, dengue fever cases have not been resolved, currently the cases that occur are greatly increased over time this happens because of factors that cause dengue fever. By considering this serious problem, the authors created a system that can predict the vulnerability level in Bandung and looks for the factors that most influence from all factors of Dengue Fever using the KNN Algorithm and Random Forest. The results of the system show the results of the best model is KNN algorithm with RMSE 29,26, and from the model shows the most influencing factors are population density, growth rate population mobility, rainfall, wind speed. by utilizing the results of the study, the government can adjust actions to each level of sub-district vulnerability and pay more attention to the factors that most influence dengue fever according to the results of the study.

Analisis Sentimen Pada Twitter KAI Menggunakan Metode Multiclass Support Vector Machine (SVM) Dhina Nur Fitriana; Yuliant Sibaroni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 5 (2020): Oktober 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Information in form of unstructured texts is increasing and becoming commonplace for its existence on the internet. This information is easily found and utilized by business people or companies through social media. One of them is Twitter. Twitter is ranked 6th as a social media that is widely accessed today. The use of Twitter has the disadvantage of unstructured and large data. Consequently, it is difficult for business people or companies to know opinion towards service with limited resources. To Make it easier for businesses know the public's sentiment for better service in the future, public sentiment on Twitter needs to be classified as positive, neutral, and negative. The Multiclass Support Vector Machine (SVM) method is a supervised learning classification method that handles three classes classification. This paper uses One Against All (OAA) approach as a method to determine the class. This paper contains the results of classifying OAA Multiclass SVM methods with five different weighting features unigram, bigram, trigram, unigram+ bigram, and word cloud for analyzing tweet data, finding the best accuracy and important feature when processed with large data. The highest accuracy is the unigram TF-IDF model combined with the OAA Multiclass SVM with gamma 0.7 is 80.59.

Multi Aspect Sentiment of Beauty Product Reviews using SVM and Semantic Similarity irbah salsabila; Yuliant Sibaroni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Beauty products are an important requirement for people, especially women. But, not all beauty products give the expected results. A review in the form of opinion can help the consumers to know the overview of the product. The reviews were analyzed using a multi-aspect-based approach to determine the aspects of the beauty category based on the reviews written on femaledaily.com. First, the review goes through the preprocessing stage to make it easier to be processed, and then it used the Support Vector Machine (SVM) method with the addition of Semantic Similarity and TF-IDF weighting. From the test result using semantic, get an accuracy of 93% on the price aspect, 92% on the packaging aspect, and 86% on the scent aspect.

Aspect Level Sentiment Analysis on Zoom Cloud Meetings App Review Using LDA Janu Akrama Wardhana; Yuliant Sibaroni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

During the Covid-19 pandemic, almost all community activities are conducted from home. Therefore, video conference technology is needed for people to carry out their normal activities from home. One of the video conference applications is ZOOM Cloud Meetings. Applications certainly have been reviewed given by their users as a reference for new users and companies of the application to know the application’s performance. However, in reviews, some constraints are the number of reviews as well as irregular. Therefore, a solution is needed with sentiment analysis that aims to classify the reviews of the application to be organized by categorizing positive or negative sentiment. In this study, aspect-based sentiment analysis was conducted on ZOOM Cloud Meetings app reviews from Google Play Store. The analysis’s result of the review data obtained three aspects, namely aspects of usability, system, and appearance. The modeling topic used is the Latent Dirichlet Allocation (LDA) method and classification using the Support Vector Machine (SVM). This research resulted in the best performance with the best parameters resulting in the performance accuracy of usability aspect is 88.83%, system aspect with 91.2%, appearance aspect with 94.78%, and performance accuracy of all aspects 91.61%.

Co-Authors Abduh Salam Adhe Akram Azhari Aditya Andar Rahim Aditya Firman Ihsan Aditya Gumilar Aditya Iftikar Riaddy Adiwijaya Agi Maulana Al Ghazali, Nabiel Muhammad Alfauzan, Muhammad Fikri Alya, Hasna Rafida Andrew Wilson Angger Saputra, Revelin Annisa Aditsania Apriani, Iklima Aqilla, Livia Naura Ardana, Aulia Riefqi Arista, Dufha Arminta, Adisaputra Nur Arya Pratama Anugerah Asramanggala, Muhammad Sulthon Atikah, Balqis Sayyidahtul Attala Rafid Abelard Aufa, Rizki Nabil Aulia Rayhan Syaifullah Aurora Az Zahra, Elita Azmi Aulia Rahman Bunga Sari Chamadani Faisal Amri Chindy Amalia Claudia Mei Serin Sitio Damar, Muhammad Damarsari Cahyo Wilogo Delvanita Sri Wahyuni Derwin Prabangkara Desianto Abdillah Devi Ayu Peramesti Dhina Nur Fitriana Dhina Nur Fitriana Diyas Puspandari Ekaputra, Muhammad Novario Ellisa Ratna Dewi Ellisa Ratna Dewi Elqi Ashok Erwin Budi Setiawan Fadhilah Nadia Puteri Fadli Fauzi Zain Fairuz, Mitha Putrianty Faiza Aulia Rahma Putra Farizi, Azziz Fachry Al Fatha, Rizkialdy Fathin, Muhammad Ammar Fatihah Rahmadayana Fatri Nurul Inayah Fauzaan Rakan Tama Feby Ali Dzuhri Fery Ardiansyah Effendi Ferzi Samal Yerzi Fhira Nhita Fitriansyah, Alam Rizki Fitriyani Fitriyani F. Fitriyani Fitriyani Fitriyani Fitriyani Gilang Brilians Firmanesha Gusti Aji, Raden Aria Gutama, Soni Andika Hanif, Ibrahim Hanurogo, Tetuko Muhammad Hanvito Michael Lee Hawa, Iqlima Putri Haziq, Muhammad Raffif I Gusti Ayu Putu Sintha Deviya Yuliani I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Indra Kusuma Yoga Indwiarti irbah salsabila Irfani Adri Maulana Irma Palupi Islamanda, Muhammad Dinan Izzan Faikar Ramadhy Izzatul Ummah Janu Akrama Wardhana Jauzy, Muhammad Abdurrahman Al Kemas Muslim Lhaksmana Kinan Salaatsa, Titan Ku Muhammad Naim Ku Khalif Lanny Septiani Laura Imanuela Mustamu Lesmana, Aditya Lintang Aryasatya Lisbeth Evalina Siahaan Made Mita Wikantari Mahadzir, Shuhaimi Maharani, Anak Agung Istri Arinta Mahmud Imrona Maulida , Anandita Prakarsa Mitha Putrianty Fairuz Muhamad Agung Nulhakim Muhammad Arif Kurniawan Muhammad Damar Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Kiko Aulia Reiki Muhammad Novario Ekaputra Muhammad Rajih Abiyyu Musa Muhammad Reza Adi Nugraha Muldani, Muhamad Dika Nanda Ihwani Saputri Naufal Alvin Chandrasa Ni Made Dwipadini Puspitarini Niken Dwi Wahyu Cahyani Novitasari, Ariqoh Nuraena Ramdani Okky Brillian Hibrianto Okky Brillian Hibrianto Pernanda Arya Bhagaskara S M Pilar Gautama, Hadid Prasetiyowati, Sri Prasetyo, Sri Suryani Prasetyowati, Sri Sulyani Prawiro Weninggalih Priyan Fadhil Supriyadi Purwanto, Brian Dimas Puspandari, Dyas Putra, Daffa Fadhilah Putra, Ihsanudin Pradana Putra, Maswan Pratama Putri, Dinda Rahma Putri, Pramaishella Ardiani Regita Rachmadania Irmanita Rafik Khairul Amin Rafika Salis Rahmanda, Rayhan Fadhil Raisa Benaya Revi Chandra Riana Rian Febrian Umbara Rian Putra Mantovani Ridha Novia Ridho Isral Essa Ridho, Fahrul Raykhan Rifaldy, Fadil Rifki Alfian Abdi Malik Riski Hamonangan Simanjuntak Rizki Annas Sholehat Rizky Fauzi Ramadhani Rizky Yudha Pratama Rizky, Muhammad Zacky Faqia Salis, Rafika Salsabila, Syifa Saniyah Nabila Fikriyah Saragih, Pujiaty Rezeki Satyananda, Karuna Dewa Septian Nugraha Kudrat Septian Nugraha Kudrat Serly Setyani Shyahrin, Mega Vebika Sinaga, Astria M P Siti Inayah Putri Siti Uswah Hasanah Sri Suryani Prasetiyowati Sri Suryani Prasetyowati Sri Suryani Sri Suryani Sri Utami Sujadi, Cika Carissa Suryani Prasetyowati, Sri Syarif, Rizky Ahsan Umulhoir, Nida Varissa Azis, Diva Azty Viny Gilang Ramadhan Vitria Anggraeni WAHYUDI, DIKI Widya Pratiwi Ali Winico Fazry Wira Abner Sigalingging Zaenudin, Muhammad Faisal Zaidan, Muhammad Naufal Zain, Fadli Fauzi ZK Abdurahman Baizal

Title

Found 153 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 153 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 153 Documents
Search