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All Journal BERKALA SAINSTEK JOIN (Jurnal Online Informatika) BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING ESTIMASI: Journal of Statistics and Its Application DedikasiMU: Journal of Community Service Narra J STATISTIKA InSTEM Tadulako Science and Technology Journal Indonesian Journal of Statistics and Its Applications
Iman Setiawan
Statistics Study Program, Mathematics And Natural Sciences Faculty, Tadulako University, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Training on Statistical Data Analysis Techniques in Classroom Action Research into Scientific Papers for Teachers of SDN Inpres 2 Talise Palu City Fadjriyani; Hartayuni; Iman Setiawan
Jurnal Inovasi Sains dan Teknologi untuk Masyarakat Vol. 2 No. 2 (2024): November
Publisher : Faculty of Mathematics and Natural Sciences, University of Jember. Jl. Kalimantan No.37, Krajan Timur, Jemberlor, Kec. Sumbersari, Jember Regency, East Java 68121

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/instem.v2i2.1521

Abstract

Writing skills are essential for teachers, especially when writing scientific papers related to their fields of expertise. However, many teachers, including those at SDN Inpres 2 Talise, Palu City, find writing scientific papers challenging. They often struggle with writing Classroom Action Research (CAR) reports and analysing the resulting data. These CAR reports are meant to be written into scientific papers, which are crucial for career advancement and functional position promotions. This community service program aims to enhance teachers' writing skills by training them in statistical data analysis techniques. The focus is on using tools such as SPSS to effectively process CAR data and independently write scientific papers. By participating in this program, teachers at SDN Inpres 2 Talise gain a deeper understanding of statistical analysis methods relevant to research writing, including basic statistical concepts, regression analysis, variance analysis , and other data analysis techniques.. The result of this service program is an increase in the ability of teachers at SDN Inpres 2 Talise, Palu City in processing data using statistical tools. Therefore, the quality of scientific papers submitted in several accredited national journals is better.
ROVIGA: Model-Driven Soil Moisture Sensor for Internet-Connected Plant Pot Setiawan, Iman; Musa, Mohammad Dahlan Th.; Nurrahma, Andi; Alfina, Alfina; Rachman, Rohis; Ariza, Moh
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8599

Abstract

The soil moisture sensor provides numerical measurements to detect changes in soil moisture using an analog voltage output. This research aims to develop a capacitive sensor based on a statistical model to detect soil moisture for plant watering, leveraging the Internet of Things (IoT). The analysis was conducted using polynomial and linear regression models. The modeling process was based on primary gravimetric test results from dried soil. The best model coefficients, selected based on the highest adjusted R-squared value, were used for sensor recalibration. A watering system was then developed using an Arduino and a model-driven capacitive soil moisture sensor integrated into an internet-connected smart plant pot, enabling remote control via a mobile phone. The research findings indicate that the 8th-order polynomial model, with the highest adjusted R-squared value of 0.9583, is the most accurate. The smart watering system using the model-driven capacitive sensor achieved soil moisture prediction outcomes ranging from 0.08 to 1.01 for 150 to 418 sensor data points. The internet-connected smart plant pot allows precise and real-time control, delivering notifications and enabling actions when plants require watering.
Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor Setiawan, Iman; Musa, Mohammad Dahlan Th.; Afriza, Dini Aprilia; Hafidah, Siti Nur
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8905

Abstract

Measuring soil moisture is possible either with directly using gravimetric test or indirectly using soil moisture sensor. Direct measurements offer accuracy but are not efficient in field measurements. On the other hand, indirect measurement offers remote measurement that will facilitate the user but lacks in accuracy. This research aims to compare and identify the best machine learning model that can improve indirect measurement (soil moisture sensor prediction) using direct measurement (gravimetric test) as a response variable. This research uses linear regression, K-Nearest Neighbours (KNN) and Decision Tree models. The three models were then compared based on Root Mean Square Error (RMSE). The results suggested that KNN (0.02939128) had the smallest RMSE value followed by decision tree (0.05144186) and linear regression model (0.05172371).
PENGEMBANGAN ANALISIS GEROMBOL BERHIRARKI DENGAN KETERGANTUNGAN SPASIAL PADA INDIKATOR MAKRO SOSIAL EKONOMI DI KABUPATEN/KOTA PROVINSI SULAWESI TENGAH Iman Setiawan; Nur’eni Nur’eni; Sritasarwati Putran
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i1.582

Abstract

This paper develops how the hierarchical clustering analysis uses multivariate variables with spatial dependence on macro social-economic indicator data in Regency/City Central Sulawesi Province. Macro social-economic indicator data used in this paper are the number of criminal cases, per capita expenditure, population density, and Human Development Index of Regency/City of Central Sulawesi Province in 2018. To answer this question, Macro social-economic indicator data was reduced to a new variable using principal component analysis. The new variable was used to identify spatial dependency using the Moran index test. Spatial weight, that meets the Moran index test on the alternative hypothesis (there is a spatial dependency between locations), was used as the spatial dependency distance. Cluster analysis using two distance including variable and spatial dependency distance. The results showed that neighboring Regency/City are in the same cluster (spatial dependency occasion). So that there are five clusters Regency/City in Central Sulawesi Province.
IMPLEMENTATION OF PLS-PM IN KNOWING THE FACTORS THAT INFLUENCE THE INCIDENCE OF TYPHOID FEVER IN PATIENTS IN ANUTAPURA PALU HOSPITAL Damayanti, Virga; Fadjryani, Fadjryani; Setiawan, Iman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1667-1680

Abstract

Typhoid fever is a multifactorial disease that has factors that can have an impact, including individual characteristics, maternal knowledge, hygiene, and nutritional status. Data on the incidence of typhoid fever involves many variables that cannot be examined directly (latent variables). This study used secondary data obtained from the medical records of typhoid fever patients at Anutapura Palu Hospital in 2023. One of the statistical methods that can be used to explain the relationship between indicators and latent variables is Partial Least Squares-Path Modeling (PLS-PM). Therefore, this study aims to model the influence of individual characteristics, maternal knowledge, hygiene, and nutritional status on the incidence of typhoid fever in patients at Anutapura Palu Hospital using PLS-PM analysis. The results of the PLS-PM analysis show that individual characteristics and nutritional status have a direct effect on the clinical images, while maternal knowledge and hygiene indirectly affect the clinical images through nutritional status, with a coefficient of determination of 0.828. So, it can be said that nutritional status is able to mediate between individual characteristics, maternal knowledge, and hygiene with the clinical images of typhoid fever.
MODELING THE IDX30 STOCK INDEX USING STEP FUNCTION INTERVENTION ANALYSIS Rais, Rais; Afriza, Dini Aprilia; Setiawan, Iman; Sain, Hartayuni; Fadjryani, Fadjryani; Junaidi, Junaidi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2057-2068

Abstract

The significant decline in the IDX30 stock index occurred due to an intervention, namely the COVID-19 pandemic, which affected market stability and investment decisions. This study aims to model and forecast the IDX30 stock index using intervention analysis with a step function, which is very suitable for capturing long-term external shocks. The methodology used includes the ARIMA (AutoRegressive Integrated Moving Average) model combined with step function intervention analysis to account for structural changes due to external disturbances. The data used is sourced from investing.com, consisting of weekly IDX30 stock index prices from January 2019 to December 2023. The results show that the COVID-19 pandemic significantly impacted the IDX30 index, causing a drastic decline. The best model identified is ARIMA (1,2,1) with intervention parameters b = 0, s = 0, and r = 1. The forecasting results range from Rp. 488 to Rp. 505, with a Mean Absolute Percentage Error (MAPE) of 1.9404%, which shows the forecasting results are very good, indicating high forecasting accuracy. These findings highlight the effectiveness of intervention analysis in modeling financial time series data affected by external disturbances.
Assessing health-related quality of life in schizophrenia patients using EQ-5D-5L index: Insights from patients and caregivers A. Prasetiyo, Nugraha; Wahyudin, Elly; Setiawan, Iman; Sanusi, Mayamariska; Purba, Fredrick D.; Arifin, Bustanul; Alkaff, Sylmina D.
Narra J Vol. 5 No. 2 (2025): August 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i2.1314

Abstract

Schizophrenia is a prevalent mental health disorder often marked by relapses, significantly affecting the health-related quality of life (HRQoL) of both patients and their families. The aim of this study was to compare the EuroQol 5-Dimension 5-level (EQ-5D-5L) responses of schizophrenia patients and their caregivers. Using an observational cross-sectional design, HRQoL was assessed among schizophrenia patients and their family caregivers recruited from a provincial referral hospital. Sociodemographic (age, sex, education, marital status, income) and clinical variables (diagnosis, treatment duration, comorbidities) were analyzed alongside HRQoL using structured interviews, medical record reviews, and the EQ-5D-5L instrument (self-report by patients and proxy-reported by family caregivers). Statistical analyses included chi-square tests for associations, Wilcoxon tests for patient-family caregiver comparisons, and multivariate modeling of HRQoL determinants. A total of 526 participants (263 patients and 263 accompanying family caregivers) were included. Significant differences were observed between patients and family caregivers in two domains: pain/discomfort and anxiety/depression. Also, the agreement between patients' and family caregivers’ reports showed good results. A substantial agreement was observed between patient-reported and family caregiver-assessed HRQoL, as indicated by a Cohen’s Kappa value of 0.8. This result suggests a strong level of consistency between the two assessments, supporting the potential use of family caregivers as reliable proxies for evaluating patient HRQoL when self-reports are unavailable or unreliable. In the self-care domain, mobility, and daily activities, patient and caregiver assessments show high agreement. In conclusion, the closeness between patients and caregivers significantly influences patients' HRQoL, providing critical insights for evaluating treatment effectiveness in schizophrenia cases. While discrepancies exist between patient and caregiver assessments, these interactions are particularly impactful in subjective domains like pain/discomfort and anxiety/depression, but not for other domains that are visible.
Co-Authors A. Prasetiyo, Nugraha Afriza, Dini Aprilia Alfina, Alfina Alkaff, Sylmina D. Andina Aulia Pramesti Ariza, Moh Borahima, Maharani Safira B. Bustanul Arifin Damayanti, Virga Elly Wahyudin Fadjriyani Fadjryani, Fadjryani Fadri, Firda Fika Reski Amaliah Hafidah, Siti Nur Hajra Rasmita Ngemba Hartayuni Hartayuni Sain Junaidi Junaidi Junaidi Junaidi Marni Sagap Mohammad Fajri Musa, Mohammad Dahlan Th. Nur Hazimah Latief Nur'Eni Nur’eni Nur’eni Nurfatra Nurpati Nurpati Nurrahma, Andi Nur’eni Nur’eni Nur’eni Nur’eni Nur’eni Purba, Fredrick D. Putri, Saskia Amalia Rachman, Rohis Rais Rais Sanusi, Mayamariska Sritasarwati Putran Yayang Matira