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p-Index From 2021 - 2026
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All Journal Serambi Engineering The Indonesian Journal of Computer Science
Ferry Astika Saputra
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Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering

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METODE STATISTIK DAN MACHINE LEARNING UNTUK PREDIKSI HARGA BAHAN POKOK DI JAWA TIMUR Dzulfiqar, Achmad Fakhri; Ferry Astika Saputra; Iwan Syarif
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4625

Abstract

Price fluctuations of basic commodities impact economic stability and community welfare. This study compares predictive methods based on statistical approaches (Simple Moving Average, Linear Regression) and machine learning techniques (Support Vector Regression, Long Short-Term Memory) using data from SISKAPERBAPO, which records daily prices of 76 basic commodities across 119 central markets in 38 districts/cities in East Java. The study supports the role of Regional Inflation Control Teams (TPID) in maintaining stable and low inflation through coordinated policies. Evaluation based on Root Mean Square Error (RMSE) and Squared Correlation indicates that SVR performs best of 4 commodities (rice, sugar, chicken meat, chicken eggs), while LSTM excels for 3 commodities (cooking oil, beef, garlic). These findings recommend SVR and LSTM as the most efffective methods for price prediction and provide a reference for TPID and policymakers in developing for price control.
Design of a Rice Drying System Prototype Based on PLC Simatic S7-1200 Mochammad Machmud Rifadil; Suryono; Ferry Astika Saputra; Achmad Akhdanizar Rafli
Jurnal Serambi Engineering Vol. 11 No. 1 (2026): Januari 2026
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rice drying in Indonesia generally still uses conventional methods by utilizing sunlight. However, weather changes often become obstacles, hampering the drying process. This research designed an automatic rice dryer based on PLC that uses PLN electricity as a power source and a heater as the heating element. The system is equipped with a sensor to monitor the moisture content of the rice so that the heater automatically turns off when the rice is dry. A DC motor is used as a drive to move the rice during the drying process. PLC programming is carried out using TIA Portal software with ladder diagram language. Based on testing for 33 minutes, the drying temperature increased from 30.7°C to 33.29°C, while the moisture content of the rice decreased from 21.20% to 13.54%, and the rice weight decreased from 1000g to 919g. These results indicate that the PLC-based drying system can control the rice drying process in a stable and effective manner. This tool is expected to be widely implemented on an agricultural scale to improve rice quality efficiently and reduce dependence on unpredictable weather conditions.
Co-Authors Achmad Akhdanizar Rafli Dzulfiqar, Achmad Fakhri iwan Syarif Rifadil, Mochammad Machmud Suryono