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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.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

In today's era, there are already many companies that have been established, from urban to rural areas, various companies have been established, especially companies that provide daily necessities such as supermarkets. And each company competes with each other in selling its products with the expected results. In this study, researchers use data to support this study. Because sales of goods or product stock can be calculated in sales results, the higher the sales, the higher the risk that will be faced. This study aims to apply data mining in analyzing sales results that occur in supermarkets. And to find out its impact on sales. This researcher uses the KNN method, by looking for test results with this method which will be implemented using the Rapid Miner application which will later produce the results of its analysis.

In today's era, there are already many companies that have been established, from urban to rural areas, various companies have been established, especially companies that provide daily necessities such as supermarkets. And each company competes with each other in selling its products with the expected results. In this study, researchers use data to support this study. Because sales of goods or product stock can be calculated in sales results, the higher the sales, the higher the risk that will be faced. This study aims to apply data mining in analyzing sales results that occur in supermarkets. And to find out its impact on sales. This researcher uses the KNN method, by looking for test results with this method which will be implemented using the Rapid Miner application which will later produce the results of its analysis.

This study aims to determine 1) the effect of implementing green accounting on the company's financial performance, 2) the effect of corporate social responsibility on the company's financial performance, and 3) the effect of implementing green accounting and corporate social responsibility on the financial performance of mining companies listed on the Indonesia Stock Exchange in 2020-2022. The method in this study uses a quantitative method. The data sources used in this study are secondary data types and the sampling technique uses the purposive sampling method by obtaining 10 companies out of 63 companies with a total of 30 annual reports listed on the Indonesia Stock Exchange in 2020-2022 which will be the object. Data analysis used in this study is descriptive statistical analysis, classical assumption tests, and simple regression tests. The results of this study indicate that the implementation of green accounting has a positive and significant effect on the company's financial performance, corporate social responsibility has a positive and significant effect on the company's financial performance and the implementation of green accounting and corporate social responsibility also simultaneously has a positive and significant effect on the company's financial performance

Indonesia is an archipelagic country that has abundant mineral wealth, both metal minerals and other minerals. Metal minerals, including gold, tend to have a higher economic value. Based on data from the Ministry of Energy and Mineral Resources, the world's gold reserves in 2020 were 50,300 tons of Au. Indonesia is among the 5 largest in the world with 5% of the total gold reserves, which is 2,600 tons of Au. Therefore, it is necessary to develop technology and science in the mining industry, especially gold and silver, to improve the optimization of the process and increase the added value of the mining products themselves. One of the gold extraction processes can be done hydrometallurgically with the cyanidation method using a intensive leach test. The results of this experiment show that factors such as cyanide usage and ultra fine particle size will greatly affect the recovery of gold and silver metals.

In today's world of retail and technology, competition is fiercely competitive. With the development of retail businesses increasing in number and mushrooming in a region, consumer needs are increasing, and retail business players are competing to develop their businesses by utilizing existing technology. Daily sales transaction data continues to increase, causing a lot of storage. Toko Ira has more than 228 sales transaction data records from 2023 to 2024 that have not been used. Data requires a lot of storage space. Additionally, the data has not been used in an effective way. Based on this problem, this research aims to use data mining to classify sales transaction data to determine which items are selling best. This research is a case study with a qualitative approach. This research was conducted with the Naive Bayes method and Rapidminer was used. The results of the sales transaction data classification research are the division of products into best-selling and non-selling categories. The results of this research show that the K-Nearest Neighbors (KNN) algorithm with a 50:50 data division is more effective in predicting and classifying sales of best-selling and non-selling products in IRA stores. The results show that the Naive Bayes algorithm has an accuracy of 89.91%, while the K-Nearest Neighbors (KNN) algorithm has an accuracy of 60.09%.

Rock mining along the Bone River, Bone Bolango Regency, contributes to infrastructure development but also has negative environmental impacts. This study aims to analyze the effects of mining activities on river environmental quality, particularly water discoloration, erosion, river widening, and material accumulation. Data obtained indicate that mining activities cause water pollution characterized by discoloration due to increased sediment and mining waste. Moreover, rock excavation accelerates erosion along riverbanks, resulting in land degradation that affects the stability of surrounding areas. Mining activities also trigger river widening, altering the river's natural morphology and increasing flood risks. The accumulation of mining residues around the river worsens conditions by obstructing water flow, causing sedimentation, and damaging aquatic habitats. The analysis highlights the need for better environmental management to mitigate the adverse effects of rock mining. Recommendations include implementing strict environmental policies, monitoring mining operations, conducting reclamation, and ensuring sustainable waste management. Additionally, involving local communities in river rehabilitation efforts can expedite environmental recovery and sustain ecosystem balance. This study is expected to serve as a reference for policymakers in managing mining activities sustainably and preserving the Bone River ecosystem.

PT Astra International BMW Semarang is a company operating in the automotive sector with 3 supporting pillars, namely Sales, Aftersales and Spare Parts for BMW car units. The availability of spare parts is one of the determining factors for consumer satisfaction with the company because if the spare parts stock is empty it will cause consumer disappointment with the company. By using spare parts sales transaction data for the period January 2019 – June 2023, totaling 52,162, it will be utilized using data mining association techniques with the a priori algorithm and the eclat algorithm. The problem in this research is how to find out consumer purchasing patterns so that there is no shortage or empty stock of spare parts in the warehouse. This research aims to determine the association of spare parts purchasing patterns in sales transactions so that partman get recommendations in making decisions about providing priority types of spare parts. This research methodology uses CRISP-DM (Cross-Industry Standard Process for Data Mining) and is implemented with the R programming language with R studio software. In 3 trials using the Apriori algorithm and 3 trials with the Eclat algorithm, The result with the highest confidence appears in a combination of 3 itemsets with minimum support 0.01 and confidence 0.9, namely if a customer buys B11.42.8.593.186 (Set oil-filter Mx) and B83.12.5.A1A.683 (Washer Cleaner) then they will also buy Z99000000333 ( BMW Engine Oil) with confidence 1.00 or 100%. From the results of this association's analysis, it can be used as advice for the management of PT Astra International BMW Semarang in managing spare parts stock.

This study aims to know the form and form of moral values contained in the novel of the love of ayu utami in her daily life. the method used is qualitative. The object of research in the novel love story of entrico, this study will focus on analyzing moral values in the novel. Data collection techniques are reading and notes. The reading technique is carried out by reading the story novel of enriek repeatedly and as a whole to clearly understand the contents of the story novel Enico by Utami's love story. Then the next step is to look for moral values in the novel langakah the next is to record the moral values contained in the novel Enico love story. This recording technique is to determine and analyze moral values in the novel of Enico's love story. The results of the study showed that the novel of the love story of Enico by ayu Utami includes, moral, religious, and education values. The moral value that we learn in Enrico love story is, as humans should have a clear life goal so that in the future we get life and kebahagin. The religious value is seen from his parents who are one religious Muslim and others are Christians, then Enrico himself does not choose a religion and belief. The value of education, namely Enrico, continues his education at a university in one of the universities in Bandung which is known as ITB, majoring in mining.

This study aims to know the form and form of moral values contained in the novel of the love of ayu utami in her daily life. the method used is qualitative. The object of research in the novel love story of entrico, this study will focus on analyzing moral values in the novel. Data collection techniques are reading and notes. The reading technique is carried out by reading the story novel of enriek repeatedly and as a whole to clearly understand the contents of the story novel Enico by Utami's love story. Then the next step is to look for moral values in the novel langakah the next is to record the moral values contained in the novel Enico love story. This recording technique is to determine and analyze moral values in the novel of Enico's love story. The results of the study showed that the novel of the love story of Enico by ayu Utami includes, moral, religious, and education values. The moral value that we learn in Enrico love story is, as humans should have a clear life goal so that in the future we get life and kebahagin. The religious value is seen from his parents who are one religious Muslim and others are Christians, then Enrico himself does not choose a religion and belief. The value of education, namely Enrico, continues his education at a university in one of the universities in Bandung which is known as ITB, majoring in mining.

This study aims to analyze and compare the financial ratios of three mining companies, namely PT Aneka Tambang, PT Adaro Energy, and PT Baramulti Suksessarana, in assessing company stability and growth. The analysis was conducted using a qualitative descriptive approach by utilizing financial ratio data such as liquidity, activity, solvency, profitability, and market in the 2019-2023 period. The results show that PT Aneka Tambang excels in liquidity stability, PT Baramulti Suksessarana has high efficiency in asset utilization, and PT Adaro Energy offers great growth potential with significant fluctuations in profitability. This research provides strategic insights for investors in selecting mining companies according to risk profiles and investment objectives, as well as a reference for academics in understanding the dynamics of the financial performance of the mining sector

This study aims to analyze the effect of audit materiality, compliance with audit standards (SA), and material misstatements on sustainability performance and audit opinion, with the principle of fairness as a moderating variable in mining companies listed on the Indonesia Stock Exchange ( IDX) for the 2019-2023 period. This research was conducted with a quantitative approach, using secondary data in the form of financial reports, sustainability reports, and independent audit reports. The results showed that compliance with audit standards has a significant effect on sustainability performance and audit opinion, while audit materiality and material misstatement do not have a significant effect directly on these two variables. In addition, the principle of fairness as a moderating variable does not strengthen the relationship between sustainability performance and audit opinion. These findings suggest that mining companies need to improve transparency and compliance with audit standards to support sustainability and obtain better audit opinions. This research provides a theoretical contribution to the study of sustainability accounting as well as practical guidance for auditors and company management in improving the quality of financial reporting and sustainability.

The financial performance of mining companies listed on the Indonesia Stock Exchange (IDX) during the 2020-20203 period was greatly influenced by fluctuations in global commodity prices and macroeconomic conditions that had an impact on the company's competitiveness and profitability. Therefore, it is important to assess how companies in this sector are managing their financial performance amid various challenges and opportunities. This study analyzes financial performance using several main financial ratios, including liquidity ratios (Current Ratio and Quick Ratio), solvency ratios (Debt to Equity Ratio and Debt to Asset Ratio), profitability ratios (Return on Assets, Return on Equity, and Net Profit Margin), and activity ratios (Total Asset Turnover and Inventory Turnover). The method used to conduct the analysis is the quantitative descriptive analysis method, using data that has been taken based on the annual financial statements of companies listed on the IDX during the period. Sample selection using the purposive sampling method, resulted in 3 companies being analyzed. The results of the analysis of 81 data observed using the Multiple Linear Regression method showed that environmental performance and environmentally friendly products had a positive impact on the company's financial performance, while environmental poroscope and environmental activities did not show a significant influence on the company's financial performance.

Indonesia, dengan tanahnya yang subur, memiliki potensi besar dalam pertanian, terutama berkat letak geografisnya di wilayah tropis yang ditandai dengan curah hujan tinggi. Kondisi ini mendukung pertumbuhan berbagai jenis tanaman secara optimal, menjadikan Indonesia dikenal sebagai negara agraris. Sebagian besar penduduknya menggantungkan hidup pada sektor pertanian. Salah satu komoditas penting adalah cabai rawit, yang memiliki rasa pedas khas dan sering digunakan sebagai bahan masakan. Selain memberikan rasa pedas yang kuat, cabai rawit juga mempercantik tampilan hidangan dan mampu meningkatkan nafsu makan. Karakteristik ini menjadikan cabai rawit sebagai elemen penting dalam kuliner Indonesia. Cabai rawit adalah salah satu komoditas utama di Indonesia. Oleh karena itu, penting untuk melakukan kajian mendalam terkait tingkat produksinya, termasuk upaya mengoptimalkan hasil produksi melalui analisis berbagai faktor yang memengaruhinya. Metode data mining memungkinkan penggalian pola-pola tersembunyi yang menarik dalam kumpulan data. Selain itu, metode ini dapat digunakan untuk mengurangi kesalahan pengguna dalam proses pengolahan data. Salah satu pendekatan data mining yang efektif untuk memetakan atau mengelompokkan data serupa adalah klastering. Klastering memiliki kelebihan unik dibandingkan metode lain karena mampu mengklasifikasikan data tanpa memerlukan pengetahuan awal. Teknik ini membagi data menjadi kelompok-kelompok berdasarkan kemiripan karakteristik. Ada berbagai algoritma yang digunakan dalam klastering, dan salah satu yang paling populer adalah algoritma K-Means

Indonesia, dengan tanahnya yang subur, memiliki potensi besar dalam pertanian, terutama berkat letak geografisnya di wilayah tropis yang ditandai dengan curah hujan tinggi. Kondisi ini mendukung pertumbuhan berbagai jenis tanaman secara optimal, menjadikan Indonesia dikenal sebagai negara agraris. Sebagian besar penduduknya menggantungkan hidup pada sektor pertanian. Salah satu komoditas penting adalah cabai rawit, yang memiliki rasa pedas khas dan sering digunakan sebagai bahan masakan. Selain memberikan rasa pedas yang kuat, cabai rawit juga mempercantik tampilan hidangan dan mampu meningkatkan nafsu makan. Karakteristik ini menjadikan cabai rawit sebagai elemen penting dalam kuliner Indonesia. Cabai rawit adalah salah satu komoditas utama di Indonesia. Oleh karena itu, penting untuk melakukan kajian mendalam terkait tingkat produksinya, termasuk upaya mengoptimalkan hasil produksi melalui analisis berbagai faktor yang memengaruhinya. Metode data mining memungkinkan penggalian pola-pola tersembunyi yang menarik dalam kumpulan data. Selain itu, metode ini dapat digunakan untuk mengurangi kesalahan pengguna dalam proses pengolahan data. Salah satu pendekatan data mining yang efektif untuk memetakan atau mengelompokkan data serupa adalah klastering. Klastering memiliki kelebihan unik dibandingkan metode lain karena mampu mengklasifikasikan data tanpa memerlukan pengetahuan awal. Teknik ini membagi data menjadi kelompok-kelompok berdasarkan kemiripan karakteristik. Ada berbagai algoritma yang digunakan dalam klastering, dan salah satu yang paling populer adalah algoritma K-Means

Riau Province is one of the largest contributors to GRDP in Indonesia with several leading sectors. This study aims to determine the leading sectors or base and non-base sectors in Riau Province. The data used in this study are secondary data obtained from the Riau Province Central Bureau of Statistics (BPS). The data was processed and analysed using the Location Quotient (LQ) analysis method. The results of Location Quotient (LQ) analysis show that there are 3 basic sectors or leading sectors in Riau Province for the 2018-2022 period. It is known that there are 3 basic sectors or leading sectors in Riau Province, the 3 sectors are the Agriculture, Forestry and Fisheries sector, the Mining and Quarrying sector, and the Manufacturing Industry sector with LQ analysis values of 2.15, 2.23, and 1.52, respectively. While the other 14 sectors are non-base sectors. For this reason, the government must better understand what sectors need to be improved and the need for cooperation from many parties so that these leading sectors can be maintained and further developed in the following year.

Data mining atau penambangan data merupakan proses pengumpulan dan pengolahan data untuk mengekstrak informasi penting. Metode data mining K-Nearest Neighbor dapat menganalisis pada aplikasi Redbus. RedBus merupakan salah satu aplikasi resmi pembelian tiket bus kota di Indonesia. Permasalahan yang muncul setelah pembaruan aplikasi RedBus adalah bertambahnya ulasan bintang satu yang menyatakan bahwa versi terbaru tidak sesuai dengan versi sebelumnya. Data Mining yang dugunakan untuk menganalisis sentimen Access by Bus Kota di seluruh Indonesia menggunakan metode K-Nearest Neighbors. Data yang digunakan adalah data yang diperoleh dari ulasan pengguna aplikasi redBus selama satu bulan terhitung dari tanggal 20 September 2024 sampai dengan 20 Oktober 2024 dengan total 1291 ulasan. Analisis sentimen pada penelitian ini menggunakan metode K-Nearest Neighbors melalui bahasa pemrograman Python. Hasil penelitian menunjukkan bahwa kinerja terbaik pada percobaan dengan pembagian data latih dan data uji, serta nilai k yang bervariasi diperoleh pada percobaan dengan pembagian 90% data latih, 10% data uji dan menggunakan nilai k = 5 dengan nilai akurasi, presisi, dan recall masing-masing sebesar 90,23%; dan nilai recall sebesar 72,38%. Klasifikasi sentimen dengan model terbaik menggunakan parameter k = 3 menghasilkan 79,26% sentimen positif, 17,25% sentimen netral, dan 3,49% sentimen negatif. 

Data mining atau penambangan data merupakan proses pengumpulan dan pengolahan data untuk mengekstrak informasi penting. Metode data mining K-Nearest Neighbor dapat menganalisis pada aplikasi Redbus. RedBus merupakan salah satu aplikasi resmi pembelian tiket bus kota di Indonesia. Permasalahan yang muncul setelah pembaruan aplikasi RedBus adalah bertambahnya ulasan bintang satu yang menyatakan bahwa versi terbaru tidak sesuai dengan versi sebelumnya. Data Mining yang dugunakan untuk menganalisis sentimen Access by Bus Kota di seluruh Indonesia menggunakan metode K-Nearest Neighbors. Data yang digunakan adalah data yang diperoleh dari ulasan pengguna aplikasi redBus selama satu bulan terhitung dari tanggal 20 September 2024 sampai dengan 20 Oktober 2024 dengan total 1291 ulasan. Analisis sentimen pada penelitian ini menggunakan metode K-Nearest Neighbors melalui bahasa pemrograman Python. Hasil penelitian menunjukkan bahwa kinerja terbaik pada percobaan dengan pembagian data latih dan data uji, serta nilai k yang bervariasi diperoleh pada percobaan dengan pembagian 90% data latih, 10% data uji dan menggunakan nilai k = 5 dengan nilai akurasi, presisi, dan recall masing-masing sebesar 90,23%; dan nilai recall sebesar 72,38%. Klasifikasi sentimen dengan model terbaik menggunakan parameter k = 3 menghasilkan 79,26% sentimen positif, 17,25% sentimen netral, dan 3,49% sentimen negatif.