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All Journal ComEngApp : Computer Engineering and Applications Journal VALENSI JURNAL KIMIA SAINS DAN APLIKASI Jurnal Natur Indonesia Teknika SEMIRATA 2015 Jurnal CoreIT Indonesian Journal of Chemistry JSFK (Jurnal Sains Farmasi & Klinis) JURNAL PHOTON JURNAL PENDIDIKAN TAMBUSAI J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Building of Informatics, Technology and Science Jurnal Yustitia JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Pengabdian kepada Masyarakat Nusantara Information System Journal (INFOS) Aksara: Jurnal Ilmiah Pendidikan Bahasa dan Sastra Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Dinamika Lingkungan Indonesia Makara Journal of Science Aurelia: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia Counseling and Humanities Review Jurnal Aplikasi IPTEK Indonesia AHKAM : Jurnal Hukum Islam dan Humaniora Mestaka: Jurnal Pengabdian Kepada Masyarakat Journal Of Artificial Intelligence And Software Engineering Journal of Hypermedia & Technology-Enhanced Learning Jurnal Kesehatan Masyarakat
Jasril Jasril
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Implementasi Metode Learning Vector Quantization (LVQ) Untuk Klasifikasi Keluarga Beresiko Stunting Aziz, Abdul; Insani, Fitri; Jasril, Jasril; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3478

Abstract

Stunting is a condition where a child's height is too short compared to children of the same age. This condition affects the health of toddlers in the short and long term, such as suboptimal body posture in adulthood, decreased reproductive health, and decreased learning capacity, resulting in suboptimal performance in school. One of the causes of stunting is a lack of nutrition, basic health facilities, and poor parenting practices. However, the current data collection and classification of families at risk of stunting still use Microsoft Excel, which is ineffective in processing large data. Therefore, the LVQ method, which is an improvement of the Vector Quantization method, is used to accelerate the classification process. In this study, 5 parameters were tested, and the optimal result was achieved by using 7 input neurons, Chebychev distance as the distance measure, a learning rate of 0.1, 7 epochs, and 30% of training data. With these parameters, an accuracy of 99.38% was obtained. Based on these results, the LVQ method can help improve accuracy in classifying families at risk of stunting
Sistem Klasifikasi Penyakit Jantung Menggunakan Teknik Pendekatan SMOTE Pada Algoritma Modified K-Nearest Neighbor Novitasari, Fitria; Haerani, Elin; Nazir, Alwis; Jasril, Jasril; Insani, Fitri
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3610

Abstract

The heart is a vital organ that plays a crucial role in pumping oxygenated blood and nutrients throughout the body. Heart disease refers to damage to the heart that can occur in various forms, caused by infections or congenital abnormalities. The World Health Organization (WHO) reports nearly 17.9 million deaths each year due to heart disease. In Indonesia, the prevalence of heart disease is around 1.5%, meaning that in 2018, approximately 15 out of 1,000 people, or nearly 2,784,060 individuals, were affected by this disease, according to the Basic Health Research data (Riskesdas) 2018. Many people have limited knowledge about heart health, leading to a lack of awareness of their heart conditions. This can be attributed to a lack of understanding regarding the importance of medical checkups related to heart health. Modified K-Nearest Neighbors (MKNN) is one of the data mining methods applied for classifying the risk of heart disease. The research utilized data obtained from the UCI dataset repository, which consists of 918 records with 12 attributes. To balance the imbalanced dataset with minority classes, the Synthetic Minority Over-sampling Technique (SMOTE) approach was used to generate new synthetic samples from the minority class. The objective of developing a web-based system for heart disease classification is to assist the public in assessing their risk of heart disease as early as possible, enabling them to take preventive actions sooner. The accuracy results of the MKNN algorithm with a 90:10 ratio are 80.37%, while with the MKNN+SMOTE approach, the accuracy increased to 84.00%. The use of the SMOTE approach improved the accuracy of low-performing data.
Penggunaan Model Bahasa indoBERT pada metode Random Forest untuk Klasifikasi Sentimen dengan Dataset Terbatas Pranata, Joni; Agustian, Surya; Jasril, Jasril; Haerani, Elin
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6335

Abstract

Masalah keterbatasan data latih menjadi tantangan utama dalam klasifikasi sentimen di berbagai bahasa, termasuk bahasa Indonesia, terutama untuk analisis sentimen terkait topik tertentu. Hal ini disebabkan oleh berbagai faktor, dan umumnya adalah kebutuhan untuk mengetahui dengan segera bagaimana sentimen terhadap suatu isu, sehingga tidak mungkin menghabiskan waktu untuk memberi label yang cukup pada data untuk proses pelatihan. Penelitian ini mengusulkan model klasifikasi sentimen dengan sumber data pelatihan yang sedikit, pada studi kasus pengangkatan Kaesang Pangarep sebagai ketua umum PSI. Algoritma Random Forest digunakan sebagai model dasar (baseline) yang dioptimasi dengan penambahan data eksternal untuk training, pemrosesan teks (text preprocessing) dan parameter tuning. Fitur input yang digunakan adalah model bahasa IndoBERT sebagai embedding kata untuk menghasilkan representasi teks yang lebih kontekstual. Hasil penelitian menunjukkan bahwa metode IndoBERT dengan Random Forest yang dioptimasi memberikan peningkatan performa yang signifikan dibandingkan baseline, sebesar 6%. Hasil klasifikasi model yang paling optimal sebesar 54% unutk F1-score dan 63% akurasi. Temuan ini menegaskan bahwa penambahan data eksternal dan optimasi parameter dapat meningkatkan kemampuan generalisasi model dalam klasifikasi sentimen bahasa Indonesia. Penelitian ini diharapkan dapat menjadi referensi metodologis bagi studi klasifikasi sentimen serupa yang menghadapi kendala ukuran dataset.
Co-Authors - Aisyah - Nurlaili -, Sulismayati Abdul Aziz Adel Zamri Adel Zamri Afrita, Indra agistia, nesa Agustian, Surya Ahmad Fauzan Aisyah Aisyah Aisyah, - Akbar, Unggul Al Fiqri, M. Faiz Alfarabi.B, Alif Ananda, Sherly Anggita, Anggi Fisi Anori, Sartika Aprilia, Tasya Asmawati Asmawati Bintal Amin Christine Jose Christine Jose Darian Alfatos Delsina Faiza Denai Wahyuni Denai Wahyuni Desviana, Laila Dewi, Erna Puspita Elin Haerani Elka Yuslinda Elvia Budianita Elviyenti, Elviyenti Fadhilah Syafria Fawrin, Heralda Fernando, Rivan Fiffah, Chainul Fitri Insani Fitria Novitasari Frischa Meivilona Yendi Guspriadi, Yan Haiyul Fadhli Haresman, Haresman Hasmalina Nasution Hendra, Rudi Hilwan Yuda Teruna Ihsan Ikhtiarudin Ikhtiaruddin, Ihsan Indah Wulandari Işık, Kenan Isnaniar Iswantir M Iwan Iskandar Jufrizal Syahri Kurnia Gusti, Siska Laila Desviana Lestari Handayani Mar'arif, Muhammad Mulky Masykur Hz Masykur HZ Muhammad Affandes, Muhammad Muhammad Irsyad Muhdarina Muhdarina Muhdarina Muliani, Sarifah Muslim Syaifullah, Mhd Nazir, Alwis Nazruddin Safaat H Negara, Benny Sukma Neni Frimayanti Novianty, Riryn Nuraini Ramadhani Nurhayati Nurlaili Nurlaili Nurlaili, - Oktavia, Lola Pranata, Joni Prasetya Prasetya, Prasetya Putra, Ade Herdian Putra, Rido Rahayu, Anggi Putri Rahma Dona Rahmadini Syafri Rahmita, Rahmita Rahmiwati Hilma Ramadhani, Eka Ramadhani, Siti Rudi Hendra Sardi, Hajra Sari, Meidita Kemala Sari, Nila Puspita Silvera Devy Siregar, Sri Hilma Soeci Izzati Adlya Sofyan Husein Siregar Sulismayati - Suwanto Sanjaya Sya'ban, Andrian Nur Thamrin Tri Windarti Tri Windarti Tria Harlianti Triana, Yeni Veithzal Rivai Zainal Wan, Xue Wenni, Osriza Wulan Sari, Wulan Yasthophi, Arif Yeni, Wila Mutiara Yuana Nurulita Yuca, Verlanda Yuharmen - Yum Eryanti Yuni Fatisa Zadrian Ardi Zahra, Fadhila Az Zahri, Firman