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Teguh Susyanto
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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 240 Documents
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IMPLEMENTASI ALGORITMA NAIVE BAYES DALAM ANALISA SENTIMEN TERHADAP TREND TIKTOK Wahyu Tisno Atmojo; Ericka Keisya; Afifah Trista Ayunda
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 Sabila Putri Adriatasya; Dedi Suhendro
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 Vivi Dian Pratiwi; Didik Nugroho; Sri Hariyati Fitriasih; Dwi Remawati
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.
MODEL CERDAS PENGENALAN POLA WARNA MENGGUNAKAN ARSITEKTUR BAM KONTINU BERBASIS NEURAL NETWORK Sestri Novia Rizki; Vani Maharani Nasution; Ahmad Taufik
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.v13i2.1027

Abstract

Color pattern recognition is an important field in digital image processing that has various applications, such as in object identification systems, image classification, and computer-based visual recognition. This study aims to design and implement a color pattern recognition system using Artificial Neural Networks (ANN) with the continuous Bidirectional Associative Memory (BAM) method. The continuous BAM method was chosen because of its ability to perform a bidirectional association process between input patterns and target patterns adaptively and stably. The research stages include collecting color data in RGB format, normalizing input values, forming an association matrix, training the network, and testing the system on a number of predetermined color patterns. The test results show that the continuous BAM model is able to recognize color patterns with a fairly high level of accuracy and a relatively fast convergence time. This system also shows resilience to small changes in color intensity values, so it has the potential to be applied to image recognition applications that require accurate color identification. Of the four color patterns resulting from the calculation, there are 2 patterns that match the target, namely the red and blue color patterns with the final target values [-1,1] output [-6,6] and [-1,-1] output [-6,-18].
PENGEMBANGAN SAVORYAI: SISTEM REKOMENDASI RESEP MASAKAN BERBASIS BAHAN DAN PREFERENSI KALORI MENGGUNAKAN CONTENT-BASED FILTERING DAN OPENAI API Ahmad Irfan Faiz; Dziky Ridhwanullah
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.v13i2.1023

Abstract

The high volume of leftover cooking ingredients in households often leads to food waste due to a lack of ideas or references for processing them. This research aims to develop SavoryAI, an intelligent web-based recipe recommendation system that suggests relevan Indonesian dishes based on user-inputted ingredients and calorie preferences. The system integrates Content-Based Filtering (CBF) with cosine similarity and TF-IDF weighting to match user-selected ingredients with recipes in the database. Additionally, OpenAI’s GPT-4o model is utilized to identify food ingredients from uploaded images. The system is implemented using Laravel, Livewire, and TailwindCSS, with data gathered through interviews with household actors and literature reviews. Evaluation was conducted through functional testing (black-box), validity testing, and confusion matrix analysis, using response from household users to determine ground truth. The results show a high accuracy in generating relevant recipe recommendations, with a precision of 1.00, recall of 0.83, and F1-score of 0.91. The results show a high accuracy in generating relevant recipe recommendations. The integration of AI image recognition further enhances usability by enabling automatic ingredient input. The finding highlight the system’s effectiveness in reducing food waste and supporting sustainable cooking practices through personalized recipe suggestions.
DECISION SUPPORT SYSTEMS CERDAS UNTUK PEMILIHAN BIBIT CENGKEH UNGGUL DALAM KONSEP SMART AGRICULTURE Andri Yunaldi; Yendrizal -
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.v13i2.1039

Abstract

The recent development of artificial intelligence (AI) technology has encouraged the use of intelligent systems in various sectors, including agriculture. One of the challenges faced by clove farmers is determining superior seeds with optimal growth potential. Selecting appropriate seeds is crucial because it directly impacts productivity and plant resilience to environmental conditions. Based on this, this research focuses on developing an intelligent Decision Support System (DSS) to assist in the selection of superior clove seeds by implementing the Simple Additive Weighting (SAW) method integrated into the Smart Agriculture concept. In this system, the decision-making process is based on several key criteria: pest infestation, growing medium moisture, seedling age, leaf color, leaf number, stem diameter, and seedling height. Each criterion is assigned a specific weight according to its influence on seedling quality. The SAW method is used to obtain preference scores for each seedling through data normalization and calculation of total weights. From these results, seedling number 10 obtained the highest score of 0.96, thus it is recommended as the best superior clove seedling (ranked 1). Furthermore, seedling number 13 with a score of 0.89 is ranked second, and seedling number 11 with a score of 0.88 is ranked third.
EVALUASI SISTEM INFORMASI MANAJEMEN RUMAH SAKIT (SIMRS) PADA PELAYANAN UNIT KERJA REKAM MEDIS MENGGUNAKAN METODE HOT-FIT Latifah Asmul Pangastuti; Karina Nur Ramadhanintyas; Heru Widianto
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.v13i2.1016

Abstract

Penerapan dan pengembangan SIMRS yang belum optimal dapat menimbulkan hambatan serta menurunkan mutu pelayanan kesehatan, sehingga diperlukan evaluasi sistem untuk mengetahui penyebab permasalahan dan menemukan solusi yang tepat. Studi ini bertujuan untuk menganalisis implementasi SIMRS Trustmedis pada unit kerja rekam medis dengan metode HOT-Fit melalui pendekatan survei kuesioner secara cross sectional. Populasi penelitian terdiri dari 200 petugas pengguna SIMRS Trustmedis, dan berdasarkan rumus Slovin diperoleh sampel sebanyak 66 responden. Uji instrumen meliputi pengujian reliabilitas dan validitas, sedangkan analisa data menggunakan regresi linear berganda. Hasil pengujian T menunjukkan bahwa variabel kepuasan pengguna pada aspek human serta seluruh variabel pada aspek technology berpengaruh signifikan, sementara hasil pengujian F mendapatkan nilai F hitung sebesar 44,106 yang mengindikasikan bahwa seluruh variabel secara simultan berpengaruh dengan kontribusi sebesar 91,9% berdasarkan uji koefisien determinasi. Kesimpulannya, aspek teknologi mampu memengaruhi net benefit, sementara aspek human dan organization tidak mampu memengaruhi. Penggunaan SIMRS Trustmedis dapat dinyatakan baik apabila sistem dimanfaatkan secara optimal melalui pelatihan, pemberian pengetahuan dan materi kepada pengguna, penyusunan SOP terkait sistem, serta evaluasi sistem sebelum diterapkan secara menyeluruh. Kata kunci: Evaluasi Sistem, Metode HOT-Fit, Implementasi Sistem
PREDIKSI PENJUALAN MINYAK GORENG DENGAN METODE SINGLE MOVING AVERAGE (STUDI KASUS : TOKO SEMBAKO C&C) Muhammad Lutfi Azhari; Tri Irawati; Sri Hariyati Fitriasih; Raden Arie Febrianto
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.v13i2.1019

Abstract

Cooking oil is a strategic retail commodity; fluctuations in demand and supply often make it difficult for store managers to determine order quantities and maintain cash flow. This study aims to design and evaluate a cooking oil sales forecasting model at a C&C grocery store using a Single Moving Average (SMA) as a simple, transparent, and easy-to-operate method. Historical sales data is processed over a short-term horizon; several SMA window orders are tested to balance responsiveness to recent changes and signal slippage. Model performance is assessed using Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE); validation results show a low error rate and accuracy above 80%, making it adequate as a baseline for inventory decision-making. As an applied output, the study developed a web-based application (PHP–MySQL) that facilitates data input, SMA calculation, trend visualization, and report printing, thus facilitating store staff in conducting periodic forecasts and setting reorder points. These findings confirm the suitability of SMA for the MSME/grocery store context, while also opening up opportunities for further development—for example, comparison with WMA/SES or integration with a safety stock module—to make ordering decisions more adaptive to market dynamics.
PENGEMBANGAN SPK MULTI-KRITERIA UNTUK PENILAIAN KINERJA PEGAWAI ADMINISTRASI MENGGUNAKAN METODE SAW Very Karnadi
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.v13i2.1025

Abstract

Penilaian kinerja pegawai administrasi merupakan salah satu aspek penting dalam meningkatkan efektivitas dan efisiensi organisasi. Proses penilaian yang masih dilakukan secara manual sering kali menimbulkan subjektivitas dan keterlambatan dalam pengambilan keputusan. Penelitian ini bertujuan untuk mengembangkan Sistem Pendukung Keputusan (SPK) multi-kriteria yang dapat membantu pihak manajemen dalam melakukan penilaian kinerja pegawai administrasi secara objektif dan terukur. Metode yang digunakan adalah Simple Additive Weighting (SAW), yang mampu memberikan hasil perhitungan berdasarkan bobot dan nilai dari setiap kriteria penilaian. Kriteria yang digunakan dalam penelitian ini meliputi Produktivitas, Kualitas Pekerjaan, Absensi, Kedisiplinan, Kerjasama, Pelayanan, dan Kepatuhan Tepat Waktu. Hasil pengujian menunjukkan bahwa sistem yang dikembangkan mampu menghasilkan peringkat kinerja pegawai secara akurat dan konsisten dengan penilaian manual. Dengan adanya sistem ini, proses evaluasi kinerja menjadi lebih efisien, transparan, dan mendukung pengambilan keputusan manajerial yang tepat.
PERBANDINGAN METODE SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR DALAM ANALISIS SENTIMEN ULASAN APLIKASI MyXL Raka Aji Nugroho; Dwi Remawati; Teguh Susyanto; Wawan Laksito Yuly Saptomo
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 14, No 1 (2026): Jurnal Tikomsin, Vol 14, No.1, April 2026
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v14i1.1062

Abstract

Sentiment analysis is an essential tool for understanding user perceptions of mobile applications, especially when reviews are unstructured text. This study aims to analyze user reviews of the MyXL application using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) and to compare their performance in classifying sentiments into positive, negative, and neutral categories. The dataset was obtained from Google Play Store via Kaggle and underwent text preprocessing, including case folding, removal of numbers and punctuation, tokenization, stopword removal, normalization, and stemming. Features were transformed into numerical representations using TF-IDF, and the data was split into training (70%) and testing (30%) sets. Evaluation using accuracy, precision, recall, and F1-score showed that SVM outperformed KNN with an accuracy of 0.743 versus 0.64, particularly in classifying neutral reviews. KNN exhibited higher misclassification in positive and negative classes, while SVM was more stable but tended to be biased toward the neutral class. These results provide insights for application developers to better understand user satisfaction and guide service improvement and feature development.

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