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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Informasi Interaktif Journal of Intelligent Software Systems
Liyan, Sur
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Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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PEMBUATAN PROTOTIPE ALAT PENDETEKSI LEVEL AIR MENGGUNAKAN ARDUINO UNO R3 sofyan sofyan; Catur Budi Affianto; Surliyan Surliyan
Informasi Interaktif Vol 1, No 2 (2016): Jurnal Informasi Interaktif Vol. 1 No. 2 November 2016
Publisher : Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1054.853 KB)

Abstract

The prototype of this water level detector uses the Arduino Uno R3 Microcontroller based on the ATmega328P chip to detect the water level. For the output of its arduinonya connected to the LED light (light), the busser (sound) and serial are connected to the laptop to display the readings from the water level, whether the water level is safe or dangerous.Key words: water level, arduino, microcontroller
Lecture Scheduling Using Genetic Algorithm Method Liyan, Sur; Kriestanto, Danny; Ramadhan, Alfitra; Haries, Muhammad; Lukman, Lukman
Journal of Intelligent Software Systems Vol 3, No 2 (2024): December 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i2.1501

Abstract

Lecture scheduling at a university is a very important element, because it determines the progress of the lecture activity process. At the Indonesian Digital Technology University, the lecture scheduling process still uses Microsoft Excel, this is considered less than optimal because it takes a relatively long time, the process is long and requires a high level of accuracy, which is something that often becomes an obstacle in the scheduling process. The genetic algorithm is an algorithm that can be used to solve problems on a large scale and with a high level of complexity, such as lecture scheduling. Genetic algorithms have advantages over other optimization methods, namely that genetic algorithms can optimize problems with complex problems and a very wide search space. There are several stages in a genetic algorithm, namely: initial population initialization, fitness evaluation, selection, crossover and mutation. The results of this research show that scheduling lectures using the genetic algorithm method results in faster and more accurate results, because the process is carried out by the program by finding the best solution from each generation iteration and the process will stop when the required solution is obtained. Meanwhile, scheduling lectures using MS Excel takes longer because it is done manually with the help of the VLOOKUP formula and requires a high level of accuracy so that there are no conflicting lecture schedules. From the test results, using Python software with a genetic algorithm takes 0.609356 seconds with an accuracy level of 100%. Meanwhile, testing using MS Excel with VLOOKUP takes around 20 minutes with an accuracy rate of 95%.Keywords— Scheduling, Lectures, Genetic Algorithm
STRATEGI PERSONALISASI PRODUK UMKM KULINER TRADISIONAL DI YOGYAKARTA DENGAN METODE RFM DAN K-MEANS CLUSTERING Liyan, Sur; Yulianto, Dwi Hery; Nugroho, Agung Yuliyanto
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4324

Abstract

Abstract: Traditional culinary Micro, Small, and Medium Enterprises (MSMEs) face significant challenges in retaining customers amidst digital competition. This study aims to formulate an effective product personalization strategy for MSMEs by utilizing customer transaction data. We applied the RFM (Recency, Frequency, Monetary) model combined with the K-Means Clustering algorithm to transaction data from 200 culinary MSME customers in Yogyakarta. The analysis results show that customers can be grouped into four homogeneous segments: VIP Customers, Loyal Customers, Potential Customers, and Risky Customers. Based on the unique characteristics of each segment, relevant marketing strategies are formulated, such as exclusive loyalty programs for VIP Customers and reactivation campaigns for Risky Customers. This study contributes by providing a practical, data-driven methodology for MSMEs to improve their retention and marketing effectiveness, filling the existing research gap in the traditional culinary MSME sector. Keyword: RFM (Recency Frequency, Monetary),K-Means Clustering, VIP Abstrak: Usaha Mikro, Kecil, dan Menengah (UMKM) kuliner tradisional menghadapi tantangan besar dalam mempertahankan pelanggan di tengah persaingan digital. Penelitian ini bertujuan untuk merumuskan strategi personalisasi produk yang efektif bagi UMKM dengan memanfaatkan data transaksi pelanggan. Kami menerapkan model RFM (Recency, Frequency, Monetary) yang dikombinasikan dengan algoritma K-Means Clustering pada data transaksi dari 200 pelanggan UMKM kuliner di Yogyakarta. Hasil analisis menunjukkan bahwa pelanggan dapat dikelompokkan menjadi empat segmen homogen: Pelanggan VIP, Pelanggan Setia, Pelanggan Potensial, dan Pelanggan Berisiko. Berdasarkan karakteristik unik setiap segmen, dirumuskan strategi pemasaran yang relevan, seperti program loyalitas eksklusif untuk Pelanggan VIP dan kampanye reaktivasi untuk Pelanggan Berisiko. Penelitian ini berkontribusi dengan menyediakan metodologi praktis berbasis data bagi UMKM untuk meningkatkan retensi dan efektivitas pemasaran mereka, mengisi kesenjangan penelitian yang ada pada sektor UMKM kuliner tradisional.
Co-Authors Catur Budi Affianto Danny Kriestanto, Danny Haries, Muhammad Lukman Lukman Nugroho, Agung Yuliyanto Ramadhan, Alfitra sofyan sofyan Yulianto, Dwi Hery