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Teguh Susyanto
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teguh@sinus.ac.id
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tikomsin@sinus.ac.id
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KH Samanhudi 84-86, Laweyan, Surakarta, 57142
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INDONESIA
Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara)
Published by STMIK Sinar Nusantara Surakarta
ISSN : -     EISSN : 26207532     DOI : http://dx.doi.org/10.30646/tikomsin
Core Subject : Science,
Computer Science & IT
Jurnal Tikomsin merupakan terbitan berkala hasil penelitian dalam bidang ilmu komputer mencakup disiplin ilmu teknologi informasi meliputi Sistem Pendukung Keputusan, Kecerdasan buatan, Data mining, Jaringan Komputer etc. Majalah ini diterbitkan secara periodik dua kali dalam setahun yaitu bulan April dan Oktober dan masing-masing terbitan sebanyak 9 artikel per issue.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 4 Documents
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Search results for , issue "Vol 13, No 2 (2025): Jurnal Tikomsin, Vol 13, No.2, Oktober 2025" : 4 Documents clear
PENERAPAN METODE K-MEANS CLUSTERING UNTUK PENGELOMPOKAN SISWA BERDASARKAN PRESTASI DI SD N 03 SANGGANG SUKOHARJO Restyanto, Dika; Sandradewi, Kumaratih; Vulandari, Retno Tri
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 13, No 2 (2025): Jurnal Tikomsin, Vol 13, No.2, Oktober 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v13i2.1007

Abstract

The rapid advancement of data science has made data processing a critical requirement across various fields, including education. Educational institutions are increasingly required to leverage available resources and information systems to enhance competitiveness and support strategic decision-making. Student achievement is generally assessed through both theoretical and practical subjects; however, determining achievement groups (very good, good, sufficient) often lacks efficiency, limiting early identification by homeroom teachers. This study applies the K-Means Clustering method to classify students' achievement levels at SD N Sanggang 03. The objective is to develop a system that assists teachers in grouping students based on performance categories—very good, good, and sufficient—thereby supporting data-driven decision-making in academic evaluation. The system was implemented using PHP and MySQL, with evaluation employing the Modified Partition Coefficient to measure clustering quality.
IMPLEMENTASI ALGORITMA NAIVE BAYES DALAM ANALISA SENTIMEN TERHADAP TREND TIKTOK Atmojo, Wahyu Tisno; Keisya, Ericka; Ayunda, Afifah Trista
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 13, No 2 (2025): Jurnal Tikomsin, Vol 13, No.2, Oktober 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v13i2.1015

Abstract

Social networking is becoming more and more important. Social media's purpose has evolved from its first appearance as a place just for self-actualization to include online buying and selling, self-actualization, and other functions. Tik tok is one of the social media platforms that is currently in high demand; opinions about its rise are mixed and include both positive and negative aspects. The goal of this study is to closely examine and comprehend how people react to the phenomena of Tiktok's development by keeping an eye on user-generated material in tweets and the evolution of sentiment over time. This experimental study suggests using the Naïve Bayes Algorithm as a sentiment analysis method to examine how Twitter users are responding to the TikTok craze. In-depth insights into the dynamics of Twitter users' reactions to the TikTok trend are sought by this research, which combines sentiment analysis technology with Confusion Matrix performance evaluation. According to the sentiment analysis results, the majority of user comments are neutral (57.03%), followed by critical (33.20%) and affirmative (9.77%) remarks. This illustrates the nuanced reactions that people have had to the TikTok movement, in which the majority of users share their ideas in an unbiased manner. The significance of this research lies in its ability to provide an answer.
PENERAPAN ALGORITMA NAÏVE BAYES UNTUK MEMPERPANJANG KONTRAK KERJA KARYAWAN PADA PT INDOSAT OOREDOO Adriatasya, Sabila Putri; Suhendro, Dedi
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 13, No 2 (2025): Jurnal Tikomsin, Vol 13, No.2, Oktober 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v13i2.1021

Abstract

Contract employees are company resources who carry out operational activities for a certain period of time based on a contract agreement. In companies that implement a contract work system, every year there are employees who get contract extensions and those who don't. Contract extensions are usually given to employees with satisfactory performance. Determining the eligibility for employee contract extensions often faces obstacles in the form of difficulty in decision making and requires a long time and process. Therefore, this study aims to assist the decision-making process by classifying employees into “Eligible” and “Ineligible” categories based on four variables, namely age, length of service, tardiness, and achievement. This study uses data from PT. Indosat Ooredoo employees as a sample with a total of five employees consisting of two classes. Based on calculations using the Naïve Bayes Algorithm, the classification results show that three employees are in the “Eligible” class and two employees are in the “Ineligible” class. This study shows an accuracy rate of 100%.
SISTEM PAKAR DIAGNOSA PENYAKIT JAMUR (CRAYFISH PLAGUE) PADA LOBSTER AIR TAWAR MENGGUNAKAN METODE CERTAINTY FACTOR BERBASIS WEB Pratiwi, Vivi Dian; Nugroho, Didik; Fitriasih, Sri Hariyati; Remawati, Dwi
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 13, No 2 (2025): Jurnal Tikomsin, Vol 13, No.2, Oktober 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v13i2.1018

Abstract

Crayfish plague, caused by the oomycete Aphanomyces astaci, is a deadly disease in crayfish/freshwater lobsters and poses a threat to lobster cultivation and sustainability. This study designed a web-based expert system to diagnose fungal disease (crayfish plague) in freshwater lobsters using the Certainty Factor (CF) method. Knowledge was gathered from observations at lobster farms and expert interviews. Seven symptoms were used and compiled as a rule base. The system was implemented using PHP and MySQL. The inference mechanism used a combination of expert and user CF, along with functional (black-box) and validity testing. The results showed a combined CF value for fungal diagnosis reached 0.9369 for the observed symptom combination. Seven scenarios tested yielded a 6/7 (85.7%) agreement. The expert system using the CF method is suitable for use as an early diagnosis tool for fungal diseases in freshwater lobsters, especially in local cultivation.

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