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All Journal PROSIDING SEMINAR NASIONAL Journal of Indonesian Tourism and Development Studies Jurnal Ilmiah Mahasiswa FEB Jurnal sistem informasi, Teknologi informasi dan komputer Al-Jami'ah: Journal of Islamic Studies Nurani: Jurnal Kajian Syariah dan Masyarakat Journal of Edugeography Format : Jurnal Imiah Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Khasanah Ilmu - Jurnal Pariwisata Dan Budaya Jurnal Ilmiah FIFO Jurnal Bina Praja IJISTECH (International Journal Of Information System & Technology) Jambura Journal of Educational Chemistry International Journal of Visual and Performing Arts Jurnal Ilmu Teknik dan Komputer Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Abdidas Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Masyarakat Indonesia Jurnal Pengabdian Kepada Masyarakat Patikala Bakti Budaya: Jurnal Pengabdian kepada Masyarakat Jurnal Arsitektur, Sains Bangunan, Kota dan Pemukiman Tuturan: Jurnal Ilmu Komunikasi, Sosial dan Humaniora Journal Collabits National Journal of Law
Mohamad Yusuf
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry 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 Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Measuring Tourist's Motivations for Consuming Local Angkringan Street Food in Yogyakarta, Indonesia Yusuf, Mohamad
Journal of Indonesian Tourism and Development Studies Vol. 5 No. 2 (2017)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jitode.2017.005.02.01

Abstract

The purpose of this study was to examine the tourist motivations for consuming local angkringan street food in Yogyakarta city, Indonesia. We distributed questionnaires to 1,514 domestic tourists from several provinces in Indonesia visiting 42 angkringan spots to determine the significance of five different motivations: cultural experience, sensory appeal, media exposure, excitement and health concern. A Confirmatory Factor Analysis was used to analyze the data. A remarkable finding showed that the items belonging to the interpersonal dimension were not grouped in one factor. The Sensory appeal has the highest level of agreement among the tourists, followed by the cultural experience. The health concern has the lowest level of agreement, which is slightly lower than the excitement motivation.Keywords: Angkringan, Food Tourism, Indonesia, Street Food, Types of Tourist Motivation.
PENERAPAN VICTORIAMETRICS SEBAGAI TIMESERIES DATABASE UNTUK MONITORING KLASTER KUBERNETES Roby Yasir Amri; Nungky Awang Chandra; Mohamad Yusuf
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.10869

Abstract

Infrastructure monitoring is a critical component in managing Kubernetes clusters, particularly for ensuring service availability and analyzing system performance. As the complexity and scale of infrastructure increase, monitoring systems are required to efficiently handle large volumes of metric data. This study aims to analyze the performance of VictoriaMetrics as a time-series database within Kubernetes monitoring systems and compare it with Prometheus based on resource usage. The research employs a quantitative approach with benchmark experiments conducted under three load scenarios: 500, 750, and 1000 target hosts. The analyzed parameters include CPU usage, memory consumption, and storage capacity. The results indicate significant differences in resource efficiency, where VictoriaMetrics maintains CPU usage between 2–10% across all scenarios, substantially lower than Prometheus, which reaches 12–24%. In terms of memory consumption, VictoriaMetrics requires only 21–27%, whereas Prometheus increases to 41–67%. For storage usage, VictoriaMetrics consumes 5–13 GB, while Prometheus requires 13–45 GB. These findings are expected to serve as a reference for organizations in selecting an appropriate monitoring solution that aligns with their Kubernetes infrastructure scale and requirements.
Analisis Sentimen Isu Artificial Intelligence di Twitter dengan SVM dan Random Forest Navidkya, Abriel; Yusuf, Mohamad
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.10037

Abstract

Artificial Intelligence (AI) has become a widely discussed topic on social media, particularly Twitter, as public opinions about this technology grow. This study aims to analyze the sentiment of Twitter posts related to AI issues using two classification algorithms: Support Vector Machine (SVM) and Random Forest (RF). The research method involves data collection via the Twitter API, followed by text preprocessing steps including case folding, tokenization, stopword removal, and stemming. The data is then manually or semi-automatically labeled with sentiments (positive, negative, neutral) to support supervised learning. Vectorization using TF-IDF is applied before training and testing the SVM and RF models to compare their classification performance. Results indicate that SVM outperforms RF in accuracy and class balance across sentiments. The application of Synthetic Minority Oversampling Technique (SMOTE) enhances performance, especially in detecting the less frequent negative sentiment. Post-SMOTE, SVM achieves an accuracy of 89.12% and an F1-score of 0.7122 for the negative class, demonstrating its ability to handle data imbalance. Although RF also improves after SMOTE, its performance remains below SVM. This study is expected to contribute significantly to public opinion monitoring and serve as a foundation for decision-making regarding AI-based technology development. 
Penerapan Metode K-Means Untuk Rekomendasi Jenis Produk Barang Perkakas Bagi Pelanggan (Studi Kasus PT.ZXY) Putri Agustianingsih; Mohamad Yusuf
Jurnal Ilmu Teknik dan Komputer Vol. 9 No. 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jitkom.v9i1.006

Abstract

Penelitian ini membahas penerapan metode K-Means dalam data mining untuk merekomendasikan produk Pekakas kepada pelanggan, dengan studi kasus pada PT.ZXY. Tujuan dari penelitian ini adalah untuk memahami dan memprediksi volume penjualan produk perkakas menggunakan metode K-Means, dengan manfaat membantu perusahaan dalam pengadaan persediaan, perencanaan produksi, dan menyediakan informasi produk yang paling banyak dibeli oleh konsumen. Metode K-Means dipilih karena potensinya dalam menganalisis strategi promosi Perkakas. Studi ini juga mencakup konsep Penemuan Basis Data Pengetahuan (KDD), Indeks Davies Bouldin (DBI), dan RapidMiner. Hasil dari praproses, pemodelan, evaluasi dan penelitian dilakukan untuk memberikan pemahaman yang komprehensif tentang pola penjualan dan potensi perbaikan strategi penjualan alat produk pada PT. PT.ZXY. Oleh karena itu, dapat disimpulkan bahwa K optimal untuk pembentukan klaster terdapat pada percobaan keenam, yaitu nilai K = 3. Nilai ini dipilih karena K =3 menghasilkan nilai DBI terkecil yaitu 0 0.328. Dimana anggota klaster 0 berjumlah 1.389 data, klaster 1 berisi 1 data dan klaster 2 berisi 39 data sehingga totalnya berjumlah 1.429 data.
Co-Authors Abdul Karim Aditiya, Eka Aditya Rizki Rinaldi Afiyati, Afiyati Agustianingsih, Putri Aji, Khusnul Bayu Akbar, Habibullah Al Fatah, M Reza Albarra, Qalbiridha Anggriawan, Guruh Pandhu Ansari, Isa Awang, Nungky Budiana, Kelik Carl Sterkens Carl Sterkens Chandra, Nungky Citron S Payu Daffa, Raihan Nur Delio, Ferdinand Defin Destha Titi Raharjana Diah Aryani, Diah Esa Esa Fadiyah, Fitri Nur Fahreza, Muhammad Rizky Fahrezi, Zidane Fami, Asrul Fernando, Henky Furqon, Nabil Ahmad Hadi Santoso Haisah, S Hidayah, Rafif Syari Indra Lesmana, Bagas Irwan Irwan Istanto, Yudha Andika Khoirunurrofik, Khoirunurrofik Kusmawan, Dicky Languju, Indri Kristin Lukito, Sebastianus Lukman Hakim Lutfansa, Nabila Aulia Moh. Muhrim Tamrin Mutiara, Sabrina Laila Naimah, Faaza Navidkya, Abriel Nia Agustina Nungky Awang Chandra Pangestu, Ridho Prabowo, Djoko Prasha, Achmad Ardani Pratama, Vemas Adi Prayuda, Sendy Putri Agustianingsih Qurotaani, Nazwa Nurul Rachmadi, Clavino Ourizqi Raditya, Nanda Garin Ramlani Lina Sinaulan Rayhan Maulana, Jericho Rifai, Alhamdi Ritonga, Andre Meyro Roby Yasir Amri Rushendra, Rushendra Sabily, Inna Safira, Dessi Adelia Salsabila, Yovi Naila Saputra, Gilas Adi Sari, Amanda Puspita Setiyaji, Andri Siti Maesaroh Sulistio, Nicholas Suranti, Meliana Syadam, Muhammad Syamsiar, Syamsiar Syamsir Alam Tofik Yanuar Chandra Virganata, Ius Andre Wibisana, Maulana Arvian Widyatamaka, Erland Wijaya, Ody Octora Yaya Sudarya Triana Yudistira, Agil Dwiki Z, M Fauzan