Publication Search

Abstract. From the perspective of Micro, Small, and Medium Enterprises (MSMEs), fluctuations in raw material prices are highly concerning as they can significantly impact business stability. While MSMEs may tolerate price fluctuations to some extent, from an industrial engineering perspective, such a passive approach contradicts the principles of continuous improvement. This study seeks to predict the price of large red chili peppers using five regression models implemented through Orange Data Mining: Linear Regression, Support Vector Machine, Decision Tree, k-Nearest Neighbors (kNN), and Gradient Boosting. Due to the limited availability of daily data, particularly within a daily timeframe, the study utilized weekly data spanning three years. The results of the Test and Score evaluation shows Gradient Boosting as the best-performing model, achieving a Mean Absolute Percentage Error (MAPE) of 0.7%. However, the MAPE for predictions in January 2025 increased to 15.8%. This error is expected to decrease as more weekly data becomes available to mitigate the inaccuracies inherent in this model. Keywords: prediction, red chilli, regression, supervised learning , orange data mining. Abstrak. Dalam perspektif UMKM, fluktuasi harga bahan baku adalah suatu hal yang paling ditakuti karena berakibat pada ketahanan usaha yang menjadi tidak menentu. Pada suatu kondisi, fluktuasi harga dapat diterima para UMKM, namun dalam perspektif teknik industri, sikap UMKM tersebut tidak sesuai prinsip continuous improvement. Penelitian ini mencoba untuk memprediksi harga cabai merah besar dengan menggunakan 5 model regresi dibantu Orange Data Mining. Yaitu Linear Regression, Support Vector Machine, Tree, kNN, Gradient Boosting. Data yang diperlukan sebagian besar tidak tersedia, khususnya dalam kerangka waktu harian sehingga penelitian ini menggunakan data mingguan selama 3 tahun. Hasil Test and Score menunjukkan model Gradient Boost terpilih menjadi model terbaik dengan tingkat MAPE 0.7% namun MAPE pada tahap Prediction di bulan Januari 2025 menjadi 15.8%. Error tersebut akan berkurang ketika data mingguan sudah cukup banyak untuk menambal kesalahan yang dihasilkan model ini Kata kunci: prediksi, cabai merah, regression, supervised learning , orange data mining.

The problem of household waste, which accounts for 50-70% of the total waste in Indonesia, mainly in the form of organic waste such as food and vegetable scraps, is a major challenge in environmental management. Composting is an effective method of managing organic waste by turning it into compost. However, organic waste management in Indonesia is still minimally practiced because it is considered to have no economic value. A 50 kg capacity composter machine equipped with a vertical mixer and using an electric motor can increase composting efficiency. This study aims to analyze the feasibility of composter machines and to analyze the effect of speed and mixing time on composter machines on the homogeneity of organic waste. The research method used is quantitative experimental with factorial experimental design (DOE) to analyze the effect of speed and stirring time on the composter machine on the homogeneity of organic waste. The stirring speed variations tested were 30, 45, and 60 RPM, with mixing times of 20, 30, and 40 minutes. The results showed that the single mixer composter machine proved to be feasible and reliable in producing homogeneous organic waste, with a high process capability value of 5.86. The use of mixing speed from 30 RPM to 60 RPM and mixing time from 20 minutes to 40 minutes significantly increased the homogeneity of organic waste, with the highest value of 97.54% at 60 RPM speed for 40 minutes.  

An expert system for diagnosing diseases in tilapia is one solution to help fish farmers detect and treat diseases that attack tilapia. In this study, an expert system was developed using the VCIRS (Voting Classifier for Integrated Rule Set) method to diagnose tilapia diseases based on the symptoms shown. The VCIRS method was chosen because of its ability to combine several classifiers to improve diagnostic accuracy. This system allows users, especially fish farmers, to enter symptoms observed in tilapia and obtain a diagnosis of possible diseases and appropriate treatment recommendations. The evaluation results showed that this system has a good level of accuracy in diagnosing tilapia diseases, by providing fast and accurate results, and making it easier for fish farmers to make decisions related to fish health. This expert system is expected to increase the productivity of tilapia cultivation by reducing the mortality rate of fish due to diseases that are not detected quickly.  

Decision Support System (DSS) is a system used to assist decision makers in determining the best alternative based on certain criteria. Determining retirement eligibility for ASN (State Civil Apparatus) employees is one of the important aspects in human resource management, where this decision affects the future of employee careers, their welfare, and state budget management. This study proposes the development of a decision support system for determining retirement eligibility for ASN employees using the Multi-Attribute Decision Making (MADM) method. The MADM method is a decision-making technique that considers several criteria and alternatives to produce optimal decisions. In this study, the criteria used to determine retirement eligibility include retirement age, length of service, work performance, health, and other factors relevant to the applicable pension policy. The method used in this system includes several steps, namely identifying criteria, assigning weights to each criterion, and evaluating alternatives based on these criteria using calculation techniques such as AHP (Analytical Hierarchy Process) or TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The results of this evaluation will provide recommendations on whether an employee is eligible for retirement or can still continue his career. The developed system is expected to facilitate the decision-making process regarding ASN employee retirement by providing objective and transparent analysis, so that the decisions taken are more appropriate and in accordance with applicable policies. Thus, this system can improve efficiency and effectiveness in human resource management in government agencies.

Indonesia's dependence on oil and gas imports has become a significant challenge to the country's economic stability, with impacts on the trade deficit, global energy price fluctuations, and fiscal dependence. This study aims to evaluate the economic impact of efforts to reduce Indonesia's dependence on oil and gas imports, focusing on the implications for economic growth, trade balance, and national energy security. The methods used include secondary data analysis, macroeconomic modeling, and simulation of the impact of energy policies such as increasing domestic oil and gas production, energy diversification, and renewable energy development. The results show that reducing dependence on oil and gas imports has the potential to reduce pressure on the trade balance and foreign exchange reserves, and improve long-term energy security. However, the transition to domestic energy security requires large investments in the renewable energy sector, supporting infrastructure, and policies that support energy efficiency. This study suggests the need for an integrated policy strategy between the government, private sector, and society to achieve the goal of reducing oil and gas dependence and improving Indonesia's economic competitiveness.

This study examines the impact of advancements in Information and Communication Technology (ICT) on cultural values, focusing on changes and the preservation of local cultures. The method used is descriptive analysis based on interviews, observations, and document studies. The results reveal that ICT has significantly influenced local cultural values, including shifts in cultural identity among younger generations, increasing individualism, and cultural homogenization due to social media. On the other hand, ICT offers opportunities for cultural preservation through digitalization and the promotion of local cultures on digital platforms. Nonetheless, challenges persist in maintaining traditional cultural values rooted in collectivism, given the dominant influence of global culture. This study recommends the need for prudent strategies in utilizing ICT to preserve local cultures without compromising their cultural identity.