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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sains dan Teknologi
Diyah Ruswanti
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Implementasi Telegram Bot sebagai Media Monitoring Gangguan Pelanggan Telkom dengan Pembaruan Berkala Berbasis Mobile Noor Arief Kurniawan; Diyah Ruswanti; Astri Charolina
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 5 (2025): Oktober 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i5.6234

Abstract

In a dynamic operational environment, especially in Telkom customer service, the process of monitoring disturbance reports is a challenge for supervisors. The disturbance reporting applications currently available are web-based, but they have not been designed responsively, so they are less user-friendly when accessed via mobile devices. Meanwhile, supervisors are required to continue conducting direct checks to ensure technician performance in the field. This research aims to develop a mobile-friendly solution in the form of a Telegram Bot that is able to retrieve regularly updated customer disturbance data from a central server, process it, and present it in a concise and easy-to-understand format via the Telegram application. The methods used include periodic data collection by helpdesk officers, data transformation into structured information, and integration of the Telegram API as a user interface. The implementation results indicate that the Telegram bot can provide information on the status of disturbance reports (new, progress, and completed), including the level of urgency, quickly and informatively, according to the latest data available. This solution is proven to support supervisor mobility in decision making and technical supervision without having to rely on web application access via desktop devices. Using Telegram as a platform also increases flexibility in accessing information in various field conditions. This research is a real contribution to the use of bot technology for the efficiency of field monitoring services based on regular updates.
COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND RANDOM FOREST ALGORITHM FOR PREDICTING HOUSING PRICES Susilo, Dahlan; Diyah Ruswanti; Supriyanta; Wawan Nugroho
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7256

Abstract

House price predictions are an important thing in the property industry and are useful for buyers in making decisions. Principal Component Analysis (PCA) and Random Forest (RF) methods were used for accuracy analysis in predicting housing prices. Purpose of this research is to measure the accuracy of both methods also to compare RF method optimized with PCA and the one that has not been optimized. The data used is house prices in Karanganyar city based on data scraping results on the rumah123.com site. The analysis reveals that Jaten has the highest number of house sales, and sales of houses with land ownership certificates are also the highest. Of the 10 variables used, land area and buildings have the most influence on selling prices. The model training results show that the RF and PCA methods combination has more optimal value than only using the RF method. The error rate of the PCA method is smaller, averaging 0.0257, making its value more consistent than using only the RF method, which has a larger error value with an average of 0.0332. The model training time using PCA is faster (5005.75) than only using the RF method (6099.25)
Co-Authors Astri Charolina Dahlan Susilo Noor Arief Kurniawan Supriyanta Wawan Nugroho