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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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Journal : JAIS (Journal of Applied Intelligent System)

 1 
Manhattan, Euclidean And Chebyshev Methods In K-Means Algorithm For Village Status Grouping In Aceh Province Amali Amali; Gatot Tri Pranoto
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7037

Abstract

The Ministry of Villages, Development of Disadvantaged Regions and Transmigration (Ministry of Villages PDTT) is a ministry within the Government of Indonesia in charge of developing villages and rural areas, empowering rural communities, accelerating the development of disadvantaged areas, and transmigration. The 2014 Village Potential Data (Podes 2014) is data released by the Central Statistics Agency in collaboration with the Ministry of Villages PDTT in unsupervised form and consists of 6474 villages in the province of Aceh. Podes 2014 data is based on the level of village development (village specific) in Indonesia by using the village as the unit of analysis. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Grouping data using k-means is done by calculating the shortest distance from a data point to a centroid point. In this study, a comparison of the distance calculation method on k-means between Manhattan, Euclidean and Chebyshev will be carried out. Tests will be performed using the execution time and the davies boulder index. From the tests that have been carried out, it is found that the number of villages in each cluster is 2,639 developing villages, 1,188 independent villages, 1,182 very underdeveloped villages, 1,266 developed villages and 199 disadvantaged clusters. The Chebyshev distance calculation method has the most efficient accumulation of time compared to Manhattan and Euclidean, while the Euclidean method has the most optimal Davies Index.
Decision Support System Recommendation Housing Using AHP And Saw Method Palangka Raya City Gatot Tri Pranoto; Ismasari Nawangsih; Edy Widodo
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7038

Abstract

Palangka Raya City, as one of the provincial capitals in Indonesia, which has an area of around 2,400,000 km2, is a strategic city as a service and distribution hub for the industrial, trade, government and education sectors. Regional Policy of the city government with the existence of a development plan in the City of Palangka Raya as an implementation of the city space with all the disadvantages of its designation resulting in a distribution pattern of urban land types which in fact is not evenly distributed throughout the city. This research was conducted based on the results of observations made in several Marketing Agents in the Palangkaraya Region, which included 5 districts where the survey results obtained several marketing agents for KPR housing with the aim of facilitating the purchase of KPR housing. The purpose of this study is to design a decision support system that is used to support the decision to purchase housing loans in the Palangkaraya area. Based on the research that has been done, it is expected that the results of the purchase decision support system for the KPR recommendation with the best value can be a recommendation for the purchase. This system is designed with the AHP and SAW methods to help prospective residents to determine the house based on the desired criteria.
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
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.
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