Pendalaman Materi (PM) merupakan kegiatan penting bagi siswa guna menghadapi ujian nasional. Ujian Nasional merupakan kegiatan pengukuran capaian kompetensi lulusan pada mata pelajaran tertentu secara nasional. Penelitian ini menggunakan 99 dataset berisikan data nilai rapor siswa yang diambil satu semester terakhir untuk menentukan kelompok (cluster) pendalaman materi terhadap mata pelajaran yang diujiankan, dengan menggunakan metode k-Means. k-Means merupakan salah satu fungsi clustering yang melakukan proses pemodelan pengelompokan data dengan sistem partisi. Ada 4 mata pelajaran yang diujiankan, yaitu; Bahasa Indonesia, Matematika, IPA, dan Bahasa Inggris. Dalam hal ini pengelompokan dibagi menjadi 2 cluster besar yaitu; kelas umum dan kelas khusus, dimana kelas khusus tersebut merupakan variable yang perlu penanganan khusus terhadap mata pelajaran yang diujiankan. Perhitungan metode ini menggunakan Rapid Miner 5.3. dengan membagi menjadi 10 cluster, dimana diperoleh nilai terbaik pada cluster 2 dan 9, dengan nilai centroid 83,5 pada cluster 9 dimata pelajaran Bahasa Indonesia.

Billing management system and services at PT. Media Prima Jaringan is still not properly and efficiently recorded. Data management and recording still uses manual processes and paper media so that errors can occur in calculating Customer Service bills. So that customer complaints often occur due to inappropriate bill values, late payments until service termination occurs. In addition to complaints, company customers will also experience losses from dissatisfied customers who switch to other providers because they stop subscribing to the service. With the development of an online billing system, it is hoped that it will make it easier for companies to manage invoices and customer data and avoid billing errors. In developing a system, systematic steps are needed to produce a good and quality information system that can be used by users. The fundamental stages in the System Development Life Cycle (SDLC) are the planning stage, the analysis stage, the design stage, the implementation stage, the testing stage and the maintenance stage. One of the SDLC development models is Rapid Application Development (RAD) which prioritizes a relatively short development time of around 60-90 days. In addition to using the RAD model for system development, the author also uses the Unified Modeling Language (UML) as a tool in the design stage. The programming language used to build this information system uses PHP (Hypertext Pre-processor) by implementing the Laravel framework and MySQL as the database. The implementation of the RAD method for the development of an online billing system takes 45 days of development. The intensity of the discussions that were carried out during development helped the programmer team complete the program design more quickly. The increase in company revenue increases after the system is running and customer complaints are also reduced.

Perusahaan retail di Indonesia memerlukan sistem informasi inventory yang efektif untuk memberikan pelayanan yang lebih baik kepada pelanggan dan meningkatkan efisiensi pengelolaan inventory. Saat ini, sistem inventory yang ada masih sederhana dan manual, sehingga rentan mengalami kesalahan dalam pengolahan data dan masalah penyimpanan dokumen. Dengan menerapkan sistem informasi inventory yang terkomputerisasi menggunakan perangkat lunak yang tepat, perusahaan dapat mengatasi masalah tersebut dan meningkatkan akurasi serta efisiensi pengelolaan inventory. Hal ini juga dapat meningkatkan kepuasan pelanggan dan membantu perusahaan menjadi lebih kompetitif di pasar. Untuk mengatasi masalah dalam sistem inventory yang sederhana dan manual, perusahaan retail di Indonesia perlu menerapkan sistem informasi inventory yang terkomputerisasi dengan menggunakan metode Rapid Application Development (RAD). Metode ini dimulai dari perencanaan, perancangan, dan implementasi dengan tujuan untuk mengembangkan sistem yang cepat, fleksibel, dan efektif. Dengan menerapkan sistem informasi inventory dengan metode RAD, perusahaan dapat mengatasi masalah seperti kesalahan dalam pengolahan data dan masalah penyimpanan dokumen serta meningkatkan efisiensi dan akurasi pengelolaan inventory. Hal ini juga dapat membantu perusahaan meningkatkan kepuasan pelanggan dan bersaing lebih efektif di pasar.

Implementing Information Technology and Archiving (PSTIK) is a field that has the main task of helping the head of service in leading and carrying out the task of managing information and communication technology. PSTIK is currently not computerized and only relies on whatsapp or telephone connections to address hardware, software or internet network issues called Ticketing Helpdesk. This application allows that at the time of the approval and processing process there is no error in communication at the DPMPTS of the DKI Jakarta Provincial Government. With this Ticketing Helpdesk information system, it is expected to be able to assist operational activities so as to support business processes in the PSTIK sector to be better than the previous system

Evaluating in determining the eligibility of giving credit is very important. Errors in providing credit worthiness assessments can result a bad credit risk. The problem that often occurs is not the application of the system by financial parties but more on HR when making predictions about the determination of consumer credit worthiness. Research in the field of computers has been done to reduce credit risk resulting in losses to the company. In this research a comparison of Logistic Regression (LR), Naïve Bayes (NB) and Decision Tree (C4.5) algorithms is performed to predict the feasibility of granting credit. In order to produce a prediction of the feasibility of granting credit to new consumers, credit data used by the company is used. The data used in this study consists of 481 consumer records that have been classified as consumers with current credit and bad credit. After testing using the same dataset on the three algorithms by comparing the AUC and Confusion Matrix values, it was found that the appropriate algorithm to be applied to the credit worthiness dataset was Logistic Regression with an Area Under Curve (AUC) value of 0.972 and Accuracy or Confusion Matrix of 93.14%. As for the Decision Tree Algorithm (C4.5) from the test results, the AUC value is 0.926 and the Accuracy is 90.85% and the Algortima Naïve Bayes AUC value is 0.905 and the Accuracy is 82.75%.