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All Journal JAIS (Journal of Applied Intelligent System) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JISA (Jurnal Informatika dan Sains) Jurnal Sistem Informasi dan Sains Teknologi Jurnal Ilmiah Wahana Pendidikan Bulletin of Information Technology (BIT) Academic Journal of Computer Science Research (AJCSR) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) JUARA: Jurnal Pengabdian Kepada Masyarakat Jurnal Pelita Pengabdian SWADIMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Gatot Tri Pranoto
Universitas Trilogi
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy 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 Social Sciences Other

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K-MEANS ALGORITHM IN CLUSTERING SALES DATA FOR CALCULATING ESTIMATED HOUSE PRICES Pranoto, Gatot Tri
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11027

Abstract

Determination of the value of the guarantee to the Bank in the process of applying for Home Ownership Credit (KPR) submitted by prospective customers still refers to the provisions of the Financial Services Authority, where the assessment must follow the existing rules and be carried out by public appraisals or commonly called the Office of Public Appraisal Services (KJPP). Currently the analyst credit officer cannot validate the results of the assessment report from KJPP, so if an error occurs either intentionally or not by KJPP or appraisal parties continue to process according to the given value. In the event of default of payment by the customer due to the lower collateral value of the loan provided, the bank violates Bank Indonesia Regulation number 18/16/PBI/2016 concerning loan to value ratio. This study aims to apply the K-Means algorithm in grouping home sales so that it can be used for the calculation of the estimated value of house prices, and develop a prototype of the house price estimation information system. Data retrieval using crawling or scrapping techniques on the website makes it easier to fulfill data on a dataset. The result of this study is the data of home sales for kebon Jeruk area spread across the internet can be grouped into 3 clusters with the value of David Bouldin Index in duri Kepa sub area, which is 0.096, in South Kedoya sub area of 0.087, in North Kedoya sub area of 0.071, and Kelapa Dua sub area of 0.117. By combining clusterization results using K-Means methodology with land price calculation formula obtained land price estimation in sub area. Keywords: K-Means, KPR, Data Scraping, KJPP, MAPPI
English Class Scheduling Information System at Indonesian-American Educational Institutions Bajsair, Faik; Baisyir, Fauzi; Pranoto, Gatot Tri
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 2 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i2.11304

Abstract

The purpose of the research is to create and implement a simple class scheduling application that is useful to minimize the occurrence of clashes of time, classes, levels, teachers and students at the same time. The research method used is the Descriptive Method with the type of case study research. The descriptive method is a method of researching the status of a group of people, an object, a set of conditions, a system of thought or an event in the present. From this Thesis or Final Project, the author can draw the conclusion that the English Class Scheduling Information System in Indonesian-American Educational Institutions is more effective, fast, conceptual, and up to date in data processing
PENERAPAN TEKNOLOGI DIGITAL DAN EDUKASI KREATIF UNTUK DAYA SAING PRODUK UMKM DESA IWUL, PARUNG, BOGOR Kaspia, Qinara Azra Puja; Putra, Fajar Ariya; Setiawan, Rifai Ady; Fida, Syafatul; Henifa, Henifa; Piliang, Tchinda Eliza; Ramadhan, Muhammad; Prijanisa, Almira Ayumi; Pranoto, Gatot Tri; Syihab, Faizah
SWADIMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT Vol 3, No 2 (2025): SWADIMAS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/swadimas.vol3no2.902

Abstract

This community service program aims to enhance the competitiveness of local MSME products in Iwul Village, Parung, Bogor, by applying digital technology and creative education. The main challenges faced by local entrepreneurs include the lack of effective digital marketing strategies and limited skills in creating visually appealing content. The implementing project conducted a series of training sessions and mentoring activities, including the use of social media, digital catalog creation, and visual content design using Canva. Additionally, creative educational activities were provided to elementary school students, and a hydroponic installation was developed to support the village's environmental aesthetics. The results showed an increase in digital marketing awareness among MSMEs and an improvement in technological skills. The creative education initiatives received positive feedback from both students and teachers. Overall, the program successfully contributed to the economic and social empowerment of Iwul Village.Kegiatan pengabdian ini bertujuan untuk meningkatkan daya saing produk UMKM Desa Iwul, Parung, Bogor melalui penerapan teknologi digital dan edukasi kreatif. Permasalahan utama yang dihadapi pelaku UMKM di desa tersebut adalah kurang optimalnya strategi pemasaran digital dan keterbatasan dalam pembuatan konten visual yang menarik. Tim pelaksana melakukan serangkaian pelatihan dan pendampingan, mulai dari penggunaan media sosial, pembuatan katalog digital, hingga pelatihan desain konten visual menggunakan Canva. Selain itu, dilakukan kegiatan edukatif berbasis kreativitas kepada siswa SD dan pembuatan instalasi hidroponik untuk mendukung estetika lingkungan desa. Hasil kegiatan menunjukkan peningkatan pemahaman pelaku UMKM terhadap pemasaran digital dan meningkatnya keterampilan masyarakat dalam menggunakan teknologi informasi. Program edukasi kreativitas juga mendapat respons positif dari siswa dan guru. Secara keseluruhan, kegiatan ini berhasil memberikan kontribusi nyata terhadap pemberdayaan ekonomi dan sosial masyarakat Desa Iwul.
Classification of Oil Loss Levels in Palm Oil Processing Using Near-Infrared Spectroscopy with Machine Learning Muhamad Ilham Fauzan; BAskara, Jaka Adi; Putri, Wahyuningdiah Trisari Harsanti; Pranoto, Gatot Tri
(JAIS) Journal of Applied Intelligent System Vol. 10 No. 1 (2025): April 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v10i1.13037

Abstract

Oil losses in palm oil processing materials, such as Final Effluent, Empty Fruit Bunches, Kernels, Pressed Fiber, and Decanter Solids, pose significant challenges in ensuring production efficiency. FOSS-NIRS technology has been proven capable of quickly and efficiently detecting oil content, but its detection accuracy requires further analytical support. This study aims to develop a machine learning model that can accurately classify FOSS-NIRS data to detect oil losses that are either above the standard (red category) or below the standard (green category). By utilizing FOSS-NIRS data across five material categories, the proposed model is expected to provide precise predictions and support decision-making in palm oil production processes. The results of the study indicate that applying machine learning methods to FOSS-NIRS data can enhance the accuracy of oil loss classification, making it a potential solution for broader implementation in the palm oil processing industry to optimize production efficiency.
Sentiment Analysis Review Threads Google Play Store with RoBERTa Model Natan Kharisma A; Lestari, Dewi; Gatot T Pranoto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.22038

Abstract

The rapid development of internet technology globally, including in Indonesia, has drastically changed communication and interaction patterns between individuals. One impact is seen in the increasing use of text-based social media applications, such as Threads, developed by Meta. Within a short time, Threads managed to attract millions of users. However, the large number of user reviews on the Google Play Store presents its own challenges, particularly in manual sentiment analysis, which is very time-consuming and prone to bias. This research aims to overcome these challenges by implementing a variant of bidirectional encoder representations from transformers (BERT), the robustly optimized BERT pretraining approach (RoBERTa) model, which has been optimized for natural language processing. The research process followed the cross-industry standard process for data mining (CRISP-DM) framework, including several main stages: understanding the business context, data exploration and model building preparation, performance evaluation, and model deployment. Data were obtained directly from the Google Play Store and then cleaned through deduplication, normalization, and tokenization stages. The RoBERTa model demonstrated strong performance, with an accuracy of 88%. Precision was recorded at 92% for positive sentiment and 81% for negative sentiment, while recall was at 88% and 87%, respectively. The F1 score was also high, at 90% for positive and 84% for negative sentiment. When compared to algorithms like naïve Bayes and support vector machine (SVM), RoBERTa proved superior. This research opens opportunities for exploring other transformer models or using ensembles to improve performance in the future.
Co-Authors Agung Nugroho Ahmad Turmudi Zy Amali Amali Annisa Maulana Majid Baisyir, Fauzi Bajsair, Faik Baskara, Jaka Adi Budiarto, Eko Dewi Lestari Donny Maulana Edy Widodo Egar Dika Santosa Faik Bajsair Fida, Syafatul Henifa, Henifa Henny Yulianti I Made Suwancita Intan Sari Rahayu Ismasari Nawangsih Kaspia, Qinara Azra Puja Majid, Annisa Maulana Maulana Majid, Annisa Maulana, Donny Muhamad Ilham Fauzan Muhammad Darwis Muhammad Ramadhan Natan Kharisma A Nawangsih, Ismasari Naya, Candra Nurhadi Surojudin Piliang, Tchinda Eliza Prijanisa, Almira Ayumi Pupung Purnamasari Pupung Purnamasari Putra, Fajar Ariya Putri, Wahyuningdiah Trisari Harsanti Raka Hikmah Ramadhan romanuddin, ahmad Roni Yusman Samidi Samidi Setiawan, Rifai Ady Syafran Nurrahman Syihab, Faizah Taofik Safrudin Tjahjanto, Tjahjanto Wahyu Hadikristanto Yaddarabullah, Yaddarabullah Yoga Religia Yusuf Eka Wicaksana