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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis Jurnal Teknik Komputer AMIK BSI JURNAL MEDIA INFORMATIKA BUDIDARMA SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Sebatik Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Building of Informatics, Technology and Science JURNAL LENTERA BISNIS G-Tech : Jurnal Teknologi Terapan REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Bulletin of Computer Science Research Journal of Information Technology (JIfoTech) Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Pengabdian Masyarakat Bhinneka Jurnal Informatika dan Multimedia Jurnal Komunikasi, Sains dan Teknologi (JKST) Jurnal Pengabdian Masyarakat UNIPOL Journal of Artificial Intelligence and Digital Business Jurnal Inovatif Wira Wacana Jurnal Sistem Informasi dan Ilmu Komputer SISFOTENIKA
Gudiato, Candra
Institut Shanti Bhuana
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Journal : G-Tech : Jurnal Teknologi Terapan

 1 
Pemodelan Decision Support System dalam Pemilihan Rumah Hunian Menggunakan Kombinasi Metode S.A.W. dan Fuzzy Logic Candra Gudiato; Christian Cahyaningtyas; Noviyanti P.
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3601

Abstract

The decision to choose a residential house is an important decision that has a long-term impact, so it requires careful and objective consideration. S.A.W. Method (Simple Addictive Weighting) and Fuzzy Mamdani are two methods that can be used to evaluate alternative residential homes based on the weights and values of specified criteria. By combining these two methods, it is hoped that the Decision Support System (DSS) can provide recommendations for the best residential houses according to user preferences. By using the S.A.W method, there are three important factors that need to be considered, namely house price, house location and land area. These three factors are then analyzed again to produce decisions or recommendations regarding the choice of residential house using the Fuzzy Mamdani method. The Fuzzy Mamdani method calculation produces a value of 50 which is tested using the Matlab application showing the same value, namely 50. From these results, it can be concluded that the S.A.W and Fuzzy Logic Mamdani methods are effective and accurate in selecting residential houses.
Analisis Sentimen Tweet untuk Mendeteksi Keinginan Bunuh Diri menggunakan Pendekatan Machine Learning pada Data Besar Noviyanti. P; Candra Gudiato; Listra Frigia Missianes Horhoruw
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6154

Abstract

Suicidal ideation is a serious mental health problem and is often difficult to detect in its early stages. Social media, especially Twitter, is one of the platforms widely used by individuals to express their feelings and emotional conditions, including expressions of suicidal ideation. This study aims to develop a machine learning model that can analyze the sentiment of tweets related to suicidal ideation using big data. The data used in this study consisted of tweets that had been processed for sentiment analysis, which were then classified into three sentiment categories, namely positive, negative, and neutral. The machine learning model applied was Naive Bayes. The results of the model evaluation showed that this model had an accuracy of 72%, with precision and recall values varying depending on the sentiment category. The highest precision was recorded in the negative and neutral categories (0.91), while the highest recall was recorded in the positive category (0.97). This study provides insight into the potential use of machine learning-based sentiment analysis to detect signs of suicidal ideation through big data from social media that can help in early detection of mental health problems.
Co-Authors Adha Maulana Aditya Pratama Cahyaningtyas, Christian Charitas Fibriani Deli, Anggelia Eko Sediyono Eligia Monixa Salfarini Hakiki, Rolland Roy Hindriyanto Dwi Purnomo Irwan Sembiring Kondang, Petronela Kurniati Kusnanto Kusnanto Kusnanto kusnanto kusnanto Listra Frigia Missianes Horhoruw Listra Frigia Missianes Horhoruw Manggu, Blasius Maya Sari Maya Sari Merryana Lestari Mira Mira Mira Mira Mira Mira Noviyanti Noviyanti, Noviyanti Noviyanti P Noviyanti P. Noviyanti. P Nurcahyo, Azriel Christian P, Noviyanti Purwanti, Benecta Ina Rupina Rupina Sabinus Beni santi thomas Silvester, Silvester Sri Yulianto Joko Prasetyo Veronika, Defi Vuspitasari, Benedhikta Kikky Wahyu Prayitno Yeremia Niaga Atlantika Yerik Afrianto Singgalen Yuliana Yuliana Yuliana