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This thesis aims to determine the effect of gross profit margin and net profit margin on stock prices in mining sector companies listed on the jakarta islamic index for 2019-2022. This study uses the indenpedent variables, namely gross profit margin and net profit margin. the dependent variable is stock price. the data used in this study is secondary data, namely in the form of financial reports of sharia companies registered on the jakarta islamic index in 2019-2022. This research is descriptive with a quantitative approach. Statistical methods using multiple linear regression analysis, t test, f test and analysis of the coefficient of determination. The results of this study indicate that the ratio of gross profit margin and net profit margin has an effect on stock prices. By using the f test it is found that the independent variables have a simultaneous influence on the dependent variables. Calculation of the coefficient of determination shows thet all independent variables, namely gross profit margin and net profit margin, can explain the stock price variable by 30% the remaining 70% is influenced by other variables not examined in this study. 

Diseases of the heart and blood vessels, such as coronary artery disease (heart attack), cerebrovascular disease (stroke), heart failure (HF), and other pathologies, are collectively referred to as cardiovascular disease (CVD). Globally, around 17 million people a year die from cardiovascular disease, with mortality increasing significantly for the first time in 50 years. Has performed an analysis of the performance of the selection algorithm with case studies predicting the determination of the customer's risk profile. Data mining is the extraction of previously unknown or previously hidden patterns from large databases or data warehouses. This study compares data mining classification models Nave Bayes, Decision Tree, Random Forest, KNN, and SVM to find the most effective model for classifying customer profile data. Later, the most accurate model will be proposed as a replacement model for forecasting the customer's risk profile. As a result, the accuracy value obtained is 82.93% and AUC is 0.896. Then accuracy testing is carried out using the rapidminer application. Testing on rapidminer was carried out with the highest accuracy obtained with an accuracy value of 86.64% and an AUC of 0.880.  

Tujuan dari penelitian ini adalah untuk menganalisis pengaruh audit fee, audit tenure dan ukuran perusahaan terhadap kualitas audit pada perusahaan pertambangan yang terdaftar di Bursa Efek Indonesia tahun 2018-2021 dengan sampel penelitian sebanyak 44 perusahaan pertambangan yang memenuhi kriteria. Dalam menganalisis data menggunakan software SPSS 22, dan metode yang digunakan adalah purposive sampling, teknik analisis data yang digunakan adalah analisis regresi logistik. Berdasarkan hasil analisis regresi logistik menunjukkan bahwa Audit Fee tidak berpengaruh terhadap kualitas audit, Tenure Audit tidak berpengaruh terhadap Kualitas Audit, Firm Size tidak berpengaruh terhadap Kualitas Audit

This The speed of information, the accuracy of data, the ease of information services, and accountability are very important reasons for the implementation of the system. Semarang University (USM) is a private university in Semarang that has the most 2 students in Central Java. Based on the 2019 USM tracer data showing horizontal alignment, namely how close the relationship between the field of study and alumni work is, it appears that there is still a discrepancy in the ability of graduates with stakeholders.  The Apriori algorithm is the best-known algorithm for finding high-frequency patterns  Rules that state associations between attributes are often called affinity analysis or market basket analysis. The use of the Apriori Algorithm in data mining calculations using data from the Semarang University tracer that the limit of the minimum support is 50% and the minimum confidence is 100% so that it forms 4 rules. From the four rules produced that modeling using the Apriori Algorithm can produce several rule formations so that it can provide an evaluation to the University for compiling steps, this can be seen because the resulting rules are different because each graduate relationship with the desired desires and different styles.  

The purpose of this study was to determine the effect of capital structure, liquidity, profit growth, and company size on earnings quality in mining sector companies listed on the Indonesia Stock Exchange for the 2019-2021 period. Earnings Quality Variables using Quality of Income. The sample method used was purposive sampling. From the population in the Mining Sector Companies during the 2019- 2021 period, 13 companies that met the sample criteria were taken. The analytical tool used is multiple linear regression analysis. Data processing in this study used the SPSS (Statistic Package for the Social Sciens) software program 25.00 for Windows. The results showed that capital structure, profit growth, and company size had no negative and insignificant effects on earnings quality. Liquidity has a positive and significant effect on earnings quality. The influence of Capital Structure, Liquidity, Profit Growth, and Company Size together has a positive and significant effect on earnings quality in mining sector companies listed on the IDX.

This study aims to determine the role of information technology in public sector accounting in preventing fraud that occurs in public sector organizations. The research conducted was a literature review research based on articles appropriate to the research topic for further analysis. The result of this research is that there are fraud prevention methods which include technical strategies and preventive strategies. The technical strategy includes efforts to prevent fraud through the use of privacy officer services, IoT access, managing data access, establishing timely reporting, and controlling data in real time. The preventive strategy is in the form of developing a fraud detection system that is run using machine algorithms, data mining, and meta learning methods.

Toko ellia umami berupaya meningkatkan produksi produknya agar lebih banyak orang mengetahuinya, mirip dengan berapa banyak orang yang membeli dan menjual produk yang sama secara bersamaan. Toko Ellia Umami menjual berbagai macam barang, yang menimbulkan kemungkinan data tidak konsisten dan tidak dapat disamakan. Beberapa produk mungkin lebih populer dari yang lain, oleh karena itu toko ellia umami harus mengetahui aturan yang mengatur penjualan dan transaksi serta produk itu sendiri. Algoritma FP-Growth adalah satu-satunya algoritma penambangan data yang digunakan untuk memeriksa persyaratan transaksi data. Di toko ellia umami, FP-Growth dapat digunakan untuk menganalisis data transaksi pembelian produk dan mengidentifikasi transaksi yang tertunda.Tujuan dari tugas akhir ini adalah mengimplementasikan algoritma FP-Growth untuk menginformasikan kepada toko ellia umami tentang rekomendasi produk dan layanan berdasarkan data dari transaksi yang melibatkan penjualan toko ellia umami. Berdasarkan hasil akhir, pola hubungan data dari toko ellia umami dengan tingkat minimum (support 0,1) dan (confiden 0,1) menunjukkan bahwa jika pelanggan membeli Libby M, pelanggan juga akan membeli Celpen dan anak.

This research leads to the Effect of the Ratio of Roa, Roe on Profit Building in Mining Companies Listed in the Jakarta Islamic Index for 2018-2022. The benefit of this research is to find out whether there is an effect of the Roa, Roe Ratio on Profit Growth in Mining Companies Registered in the Jakarta Islamic Index for 2018-2022. The analytical method used in this study is panel data regression. and processed data used in this study by utilizing Eviews 12 and Microsoft Excel 2010 software. The sampling method used was purposive sampling, namely by considering several criteria specified in this research. The research results show that Roa has an effect on profit growth and Roe has no effect on profit growth, while together (simultaneously) Roa and Roe have an effect on profit growth in mining sector companies in JII 2018-2022Keywords: net income, total assets and share price

Akomodasi merupakan orientasi sosial yang bermakna keutuhan sosial demi menjauhi serta mendamaikan kegentingan, pertikaian yang saling berkaitan. Seumpama bentuk kegiatan akomodasi yang bermanfaat mempersiapkan perlengkapan guna melengkapi keperluan. provinsi jawa barat terdapat tempat wisata yang ramai dikunjungi oleh wisatawan nusantara dan mancanegara oleh sebab itu pengunjung menggunakan fasilitas akomodasi yang tersedia di setiap daerah, wisatawan nusantara ramai menginap di villa sedangkan wisatawan mancanegara lebih memilih hotel berbintang. Penelitian ini memfokuskan bagaimana pengelompokan wisatawan daerah wisata akomodasi dan jenis wisatawan terbanyak yang ramai berkunjung menggunakan algoritma k-means, analisis ini memakai metode Knowledge Discovery in Database (KDD). Teknik pengumpulan data atau pengumpulan data bersumber dari Dinas Pariwisata dan Kebudayaan Provinsi Jawa Barat. Dengan tujuan kiranya menjadi pengetahuan untuk wisatawan serta mampu mempublikasikan wisata di media sosial dan diharapkan akan lebih meningkat jumlah wisatawan yang berkunjung dan meningkatnya pembangunan sarana dan prasarana di setiap daerah akomodas.Hasil dari penelitian ini bahwa daerah akomodasi yang ramai dikunjungi adalah Kota Bandung dengan jenis wisatawan yang datang yaitu pengunjung  dalam negeri (Nusantara).

In the business world, efforts are needed as much as possible in gaining profits. The accuracy of marketing strategies can be seen from the consumer spending pattern database obtained from sales transactions on fashion products that are usually purchased simultaneously by customers. Information about the Pattern of Purchasing Customer Shopping that is Inaccurate at the Ayu Collection Online Shop Shop has caused promotional policy to be one of the causes of the store to suffer losses. One way to get an accurate customer shopping pattern is to use data mining. One of the methods contained in data mining is the association analysis method, in the association analysis there are several algorithms, one of which is the FP-Growth algorithm. In this study several association rules were found by applying the Frequent Pattern (FP-Growth) algorithm from the transaction database Fashion sales at Ayu Collection Online Shop. This association rules will later be used as decision making material to develop successful marketing and sales strategies. The findings of this study are in the form of product recommendations, namely the proposal of two or more items based on the findings of the FP-Growth algorithm using a 50% confidence value and a minimum support of 40%, this study uses assistance from the rapidminer tools version 9.9.

Dinamika persaingan bisnis khususnya di dunia kesehatan mendorong pengusaha untuk menemukan strategi pemasaran produk obat yang efektif. Permasalahan penjualan produk obat pada Apotek Juang Jaya yang sering terjadi adalah penumpukan obat yang kurang diminati sehingga  menjadikan obat menjadi kadaluarsa. Penerapan sistem rekomendasi dapat meningkatkan penjualan obat yang diminati dan mengurangi kerugian dari produk yang kurang diminati,  dengan cara   mengolah data transaksi dan memberikan rekomenadasi obat yang memiliki keterhubungan. Algoritma Apriori atau sering disebut analisis asosiasi adalah salah satu teknik data mining untuk menemukan aturan asosiasi antara suatu kombinasi item dalam data transaksi penjualan. Aturan asosiasi ini bertujuan untuk mendapatkan kombinasi antar item dan pola pembelian konsumen. Parameter aturan asosiasi yang digunakan adalah nilai support, nilai confidence, dan pengujian menggunakan nilai lift untuk mengetahui aturan asosiasi valid atau tidak. Dalam perhitungan menggunakan algoritma Apriori, nilai support yang digunakan adalah 20%, dan nilai confidence yang digunakan adalah  60%. Hasil perhitungan pengolahan data pada Apotek Juang menggunakan algoritma Apriori mendapatkan aturan asosiasi sebanyak 7 rule, dimana hasil penghitungan korelasi positif tertinggi adalah membeli Pimtrakol, maka membeli Bodrexin Flu dengan   nilai support 22,58%, nilai confidence 77,78% dan nilai pengujian lift adalah 1,85 yang berarti aturan asosiasi valid.

The rapid growth of e-commerce in Indonesia has generated a massive and continuous volume of product reviews. This user-generated content is vital for business intelligence, yet its sheer scale makes manual analysis inefficient, subjective, and practically impossible. Automated sentiment analysis is therefore crucial for businesses to efficiently understand customer feedback and market perception. This research addresses this gap by implementing the Naïve Bayes Classifier (NBC) algorithm to automatically classify the sentiment of Indonesian-language e-commerce product reviews. This study utilized a dataset of 2,000 reviews collected from a major e-commerce platform's "Electronics" category. The data underwent critical text preprocessing stages (case folding, tokenizing, stopword removal, and stemming using the Sastrawi library) to handle the complexities of informal Indonesian text. The dataset was split using an 80/20 ratio, resulting in 1,600 training reviews and 400 testing reviews. Model performance was then evaluated using a Confusion Matrix, focusing on the key metrics of Accuracy, Precision, and Recall. The test results showed excellent performance, achieving an Accuracy of 90.00%, Precision of 91.93%, and Recall of 95.00%. These results demonstrate that the Naïve Bayes algorithm, when supported by robust preprocessing, is a highly effective, reliable, and computationally efficient method for this task, providing a valuable tool for e-commerce stakeholders.

Criminal provisions in a statutory regulation are certain doses or remedies as a means of achieving the formulated goals. The purpose of this article is to examine and analyze the inconsistency of the material content of the criminal provisions against the formulation of the objectives of legal certainty in the Law of the Republic of Indonesia No. 3 of 2020 concerning Mineral and Coal Mining. The research method uses a normative juridical method. Secondary data in the form of primary legal data on laws and regulations related to the criminal provisions of the Minerba Law is elaborated so that the adequacy of the content of the criminal provisions is able to test the clarity of the objectives of legal certainty. The results found that there were inconsistencies in criminal provisions as a means of achieving goals in the formulation of guaranteeing legal certainty. The application of Article 162 of the Mineral and Coal Law is subjective and tends to criminalize members of the community around mineral and coal mining. This fact proves that the objective of legal certainty is objective, ambiguous with subjective criminal means. The main article of criminal provisions in the Minerba Law is Article 158 which is systematically constructed with Article 35. Basically, the criminal provisions are used as the legal regime for licensing in the strategies and techniques of government control and control in the mineral and coal sector.

Indonesia merupakan negara dengan beberapa kota di setiap provinsinya, namun tentunya setiap kota mempunyai tingkat inflasi yang berbeda setiap tahunnya. Dengan banyaknya data inflasi di Indonesia, sulit bagi pemerintah untuk mengkategorikan angka inflasi. Penulis berinisiatif melakukan penelitian tentang mengelompokkan nilai inflasi pengeluaran di jawa barat. Tujuan membuat laporan ini merupakan untuk memperoleh informasi dengan kualitas terbaik dari data yang diproses. Proses ini di harapkan dapat membantu pemerintah dalam mengetahui nilai inflasi berdasarkan kelompok pengeluaran tertinggi dan terendah di Jawa Barat. Metode yang digunakan adalah metode k-means clustering. Penelitian ini juga di dukung menggunakan salah satu perangkat lunak atau tools untuk mengolah data mining yaitu RapidMiner. Didapat hasil cluster terbaik yaitu 5 cluster berdasarkan nilai DBi 0,063. Dimana yang termasuk ke dalam cluster tersebut adalah cluster 0, cluster 2, dan cluster 4 dengan tingkat inflasi rendah berjumlah 5 Kabupaten/Kota. Dan yang termasuk ke dalam tingkat inflasi tinggi adalah cluster 1 dan cluster 3 berjumlah 2 Kabupaten/Kota. Hasil dari data ini juga menunjukkan nilai akurasi sebesar 100%, sehingga bisa disimpulkan jika algoritma k-means mempunyai nilai akurasi tinggi.

Berdasarkan data yang dipublikasikan oleh Badan Penanggulangan Bencana Daerah (BPBD), Jawa Barat merupakan provinsi dengan jumlah kejadian bencana alam tertingggi di Indonesia sebanyak 3006 kejadian untuk periode 2015-2021. Kondisi ini mengharuskan BPBD (Badan Penanggulangan Bencana Daerah) dan Pemda Provinsi Jawa Barat untuk memperhatikan penanggulangan bencana serta penanganan dampak bencana. Bencana-bencana yang terjadi dapat mengakibatkan dampak yang merusak berbagai bidang. Salah satu dampak yang sangat berpengaruh adalah dampak kerusakan rumah. Kerusakan rumah akibat bencana merupakan dampak yang menyangkut kerusakan pada bidang ekonomi, sosial dan lingkungan. Karena itu, penanganan dampak kerusakan rumah harus dilakukan secara matang, tepat serta cara penanganannya harus berkembang setiap saat. Agar kedepannya bisa melakukan penanganan seperti yang diinginkan, maka perlu diketahui klaster/kelompok bencana berdasarkan dampak jumlah kerusakan rumah berdasarkan kondisi akibat bencana yang telah terjadi di Jawa Barat, dengan cara melakukan pengimplementasian algoritma K-Means clustering untuk mengklasterisasikan data jumlah kerusakan rumah berdasarkan kondisi yang diambil dari portal resmi data terbuka milik Pemda Provinsi Jawa Barat yaitu Open Data Jabar. Dalam kaitannya dengan data dampak bencana, teknik pengelompokan pada data mining sangat berguna dalam mengelompokkan data dampak bencana berupa keruskan rumah berdasarkan kemiripannya. Proyek tugas akhir ini mengimplementasikan algoritma k-means untuk mengklasterisasi data jumlah kerusakan rumah akibat bencana berdasarkan kondisinya yang terjadi di Jawa Barat dan menghasilkan 2 klaster/kelompok dengan nilai dbi teroptimal sebesar 0,118 dimana klaster 0 berisi data yang berasal dari 16 Kabupaten di Jawa Barat dan klaster 1 yang berisi data yang berasal dari 9 Kota di Jawa Barat.

Mining cryptocurrencies on the blockchain is a method for collecting crypto by breaking secret codes compiled with mathematical algorithm equations. Miners will receive rewards in the form of digital coins according to the amount of crypto mined and the type of crypto, this is because each crypto has its own level of difficulty. Crypto includes electronic money (e-money), where the use of e-money as a medium of exchange in modern transactions is still being debated among contemporary scholars about its legitimacy. Two things that become the problem are (1) what is the mechanism for mining cryptocurrency on the blockchain, (2) how is the review of Islamic Economic Law on cryptocurrency mining on the blockchain. The purpose of this study is to describe and explain cryptocurrency mining practices on the blockchain and to find out the review of Islamic Economic Law on cryptocurrency mining. This research is a qualitative research by collecting data and information through interviews. The data sources include primary data sources, namely interviews with crypto miners, secondary data sources, namely books, journals, theses, scientific papers and other written works. While the tertiary data comes from dictionaries, encyclopedias, and related articles. This research data collection method obtained from interviews, observation, and documentation. While the method used to analyze the data is a qualitative analysis method using the theory of mining, al-mal, and ju'alah. From the research results it is known: (1) The cryptocurrency mining mechanism on the blockchain is carried out with a supercomputer facilitated by a GPU, FPGA or ASIC by using certain scripts on the BTC/ETH network to find new blocks containing crypto to get rewards (sell). (2) According to the Sharia Economic Law review of mining cryptocurrency on the blockchain, crypto reward (ju'al) results can be accepted as assets according to the definition of the Hanafiyyah scholars, Ibn Abidin, Imam az-Zaila'i, Imam al-Syatibi, Ibn Arabi and Syafii scholars. However, it is not accepted as a means of payment like conventional money (gharar, dharar, qimar), but both must comply with the conditions and limits set by the Shari'a, this is stated in the Fatwa of the National Sharia Council-Indonesian Ulema Council No: 116/DSN-MUI/ IX/2017 concerning Sharia Electronic Money.

Teknologi semakin berkembang atau berkembang sangat pesat. Perkembangan teknologi juga mempengaruhi tuntutan masyarakat yang semakin meningkat sehingga toko atau supermarket berlomba-lomba menyediakan berbagai jenis produk atau kebutuhan sehari-hari. Walau supermarket pasar moderen yang menyaingi pasar tradisional, akan tetapi supermarket pun pasti akan tersaingi oleh supermarket yang lain. Setiap kota pasti ada trial yang berbeda- beda,yang dimana pelanggan pasti akan memilih mana yang sangat pantas mereka kunjungi untuk berbelanja dan mana yang tidak. Namun, di setiap toko dan supermarket perlu diketahui jenis produk apa saja yang diminati oleh pelanggan yang akan dituju dan pelayanan yang bagaimana yang mereka inginkan. Dataset supermarket ini mempunyai histori penjualan di 3 (tiga) cabang berbeda selama 3 bulan. Membutuhkan klasifikasi penjualan supaya supermarket  tersebut bisa menigkatkan perkembangnya dalam menjual barangnya. Dari hasil riset yang dilakukan di supermarket 3 cabang  tersebut terdapat 16 artibut dan 1000 record, Supermarket  menjual barang umum, elektronik, fashion, makanan dan minuman, kesehatan dan kecantikan, kebutuhan rumah dan gaya hidup, olahraga dan perjalanan. Untuk membantu toko dan supermarket, penulis akan melakukan analisis dengan mengklasifikasikan data mining pada supermarket sort dengan menggunakan algoritma naive bayes untuk mengetahui jenis produk yang disukai pelanggan di 3 cabang dalam 3 bulan. Kategori produk yang diperoleh dari mengklasifikasikan produk penjualan dapat meghasilan  akurasi mencapai 98.5%. Penggunaan  metode algoritma naive bayes dan confusional matrix memberikan pengaruh yang sangat penting dalam mendapatkan klasifikasi penjualan supermarket yang akurasi dan signifikat.

Pada periode tahun 2018 sampai dengan tahun 2021 saham PT. BSI tbk (BRIS) cenderung mengalami fluktuasi harga setiap hari, sehingga dari kumpulan data time-series saham BRIS dibutuhkan penggalian data untuk menemukan pola model prediksi yang dapat menemukan informasi yang bermanfaat. Metode Data mining atau disebut juga dengan knowledge discovery merupakan penggalian informasi yang tersimpan dalam basis data yang besar melalui studi mengumpulkan, membersihkan, memproses, dan menganalisis, sehingga mendapatkan hal yang berguna dari data saham BRIS. Adapun model yang digunakan adalah metode regresi linier berganda, dan metode ARIMA dimana kedua metode tersebut memiliki keunggulan dalam analisis data numerik yang cukup akurat. Tujuan penelitian ini adalah untuk menerapkan dan menghasilkan model persamaan yang akurat antra kedua metode tersebut dalam memprediksi harga saham PT. BSI tbk.  Hasil yang didapat adalah  model regresi linier berganda dengan hasil nilai MAPE 1,1% yaitu 98,9% lebih akurat dibandingkan dengan model ARIMA yaitu dengan hasil nilai MAPE 2,36% dan akurasinya 98,9%.

Discount is a sales strategy that lowers the price offered to buyers in the hope of increasing sales profits. However, when offering these discounts, stores often don't think about how discounted products can attract customers, so a support system is needed that can respond to the right and appropriate discounts. This support system with data mining techniques using apriori algorithm. This apriori method can search for several products that are often purchased simultaneously, therefore when juxtaposed with this study the effectiveness of the recommendations will be very good. Not only that, if it is added with a combination of recommendations with discounts, it will make customers interested to buying the product. The expected result of this research is to obtain a new sales strategy by making recommendations on items that will be used as discount packages.

ABSTRACT The rapid development of technology has a very large effect in various fields. One of them is in the field of buying and selling business, which is getting higher and higher competition between business actors. One of the strategies to increase sales is to implement a recommendation for goods, but from the various categories of goods in the store, there are products that are not in demand by customers, so that if left alone, the products that are not in demand will not sell well and will make the accumulation of goods in the store, in addition to that with the many categories, it makes some customers confused to choose products that suit their wishes buyer. Therefore, the author conducts an assessment first by conducting a literature study, which finally the purpose of this study is to make it easier for users and business owners to recommend goods in the store and determine the desired product by implementing a recommendation system on the Rameiki Mart Store website which is taken from the amount of data, this is also beneficial for store owners because the existence of this recommendation system can help as a means of product promotion , as well as in recommendations for the purchase of goods. In this study, the researcher created a recommendation system using the a priori algorithm method.