This research is about a decision support system using the Simple Additive Weighting method to help determine the recipients of social assistance wich provided by the government with the aim to improve their standard of living. Wedomartani Village has a fairly large area with a large population. The problem is that the number of potential beneficiaries is not proportional to the available social assistance quota, so it is necessary to select recipients based on certain criteria to fit the target. The Simple Additive Weighting (SAW) method has been used to determine the nomination of candidates for social assistance. From the results of data processing for prospective recipients of Bantuan Langsung Tunai Dana Desa (BLT-DD) in 2020, If the largest 25 are taken from it, it shows that the distribution of the poverty level of the population in the area is not evenly distributed, so a policy made by the sub-district government is needed for more specific poverty reduction. 

/pdf/klasterisasi-penerima-bantuan-sosial-menggunakan-metode-2q8icgfd.pdf

Klasterisasi Penerima Bantuan Sosial Menggunakan Metode Simple Additive Weighting

The Blended Learning learning model is a system that collaborates classroom face-to-face learning with online learning. This study aims to measure the acceptance of the Blended Learning learning model among students. The variables developed refer to the Technology Acceptance Model (TAM), which is a model of user acceptance of information systems. The data obtained were analyzed by descriptive statistics and qualitative analysis. The results showed that in general the use of the Blended Learning learning model could be accepted by students in the category of Good approaching is Very Good. The mean value of all aspects reache d a value of 3.94 on a scale of 5 (79%). The results of qualitative analysis were in line with the results of quantitative analysis. Students receive d good use of e-Learning while maintaining face-to-face learning activities in class. The results of this study can be used as a basis for recommending the selection of learning methods in universities

/pdf/analisis-diskriptif-technologi-acceptance-model-pada-m4s3z8t6y2.pdf

Analisis Diskriptif Technologi Acceptance Model pada Penerapan Blended Learning

Academic information system is an important system in students learning’s activities because it’s used as connector between students and lecturers. In addition, academic information system also acts as a liaison between lecturers and other relevant external parties. Academic information system provides several services, both for students and lecturers. These facilities include the provision of information on study plans, grades, schedules, and courses. However, only a few facilities are frequently used so that the use of the academic information system is not optimal. The problem in this study is the need for an evaluation to determine the acceptance of the academic information system by users, so that the system can be improved and developed. This study aims to determine the acceptance of web-based academic information system by users at STMIK Sinar Nusantara Surakarta with the Technology Acceptance Model or TAM.

/pdf/technology-acceptance-model-pada-sistem-informasi-akademik-cd2btmwb29.pdf

Technology Acceptance Model pada Sistem Informasi Akademik berbasis Web

https://iopscience.iop.org/article/10.1088/1742-6596/1179/1/012139/pdf

Early Detection of Preeclampsia using a Rule-Based System Information System

Purpose: As the complexity and diversity of cyberattacks continue to grow, traditional security measures fall short in effectively countering these threats within web-based environments. Therefore, there is an urgent need to develop and implement innovative, advanced techniques tailored specifically to detect and address these evolving security risks within web applications.Methods: This research focuses on analyzing attack detection in log access servers using machine learning classification with two primary approaches: expert labeling integration and best model selection. Expert labeling determines whether log entries are safe or indicate an attack.Result: Validation in labeling was applied using different datasets to minimize errors and increase confidence in the resulting dataset. Experimental results show that the Decision Tree and Random Forest models have nearly identical accuracy rates, around 89.3%-89.4%, while the ANN model has an accuracy of 81%.Novelty: This study proposes a fusion of expert knowledge in labeling log entries with a rigorous process of selecting the best classification model. This integration has not been extensively explored in previous research, offering a novel approach to enhancing attack detection within web applications. The research contribution lies in the integration of expert security assessment and the selection of the best model for detecting attacks on server access logs, along with validating labels using various datasets from different log devices to enhance confidence in the analysis results.

Analysis of Attack Detection on Log Access Servers Using Machine Learning Classification: Integrating Expert Labeling and Optimal Model Selection

Computer based data processing is expected to improve user performance, but the computer was not completely accepted by individuals. STMIK AKAKOM in one of their activities applied information technology to SIAKAD that provides some features for learning activities on campus
The analysis of SIAKAD acceptance by students using the technology acceptance  model (TAM), with partial least square , is to know how the behavior of SIAKAD users as the end user? It would be related to usefulness (PU), ease of use (PEOU), attitude toward using (ATU), and behavioral intention to use (BITU).
From the analysis of data, the results is mention that there were a positive and significant influence between the variables. Technology acceptance factors that inflict attitude to use SIAKAD by student in their learning activities is the ease of use and usefulness )

/pdf/analisis-penerimaan-mahasiswa-terhadap-sistem-informasi-5443z89832.pdf

Analisis Penerimaan Mahasiswa Terhadap Sistem Informasi Akademik (SIAKAD) dengan Metode Technology Acceptance Model (TAM)

Selama dipublikasikan, situs suatu institusi harus dianalisis tentang data penggunaan dan akses kunjungan pengguna. Analisis dapat dilakukan terhadap halaman yang paling banyak dikunjungi, bagaimana cara pengunjung mengakses web, apakah akses pengguna memakai desktop atau smartphone dan sebagainya. Data tersebut sangat penting bagi pengelola situs atau institusi dalam menetapkan kebijakan. Tujuan penelitian yang dilakukan adalah untuk menyusun metode penggunaan data catatan log server untuk analisis akses situs serta analisis data akses berdasarkan catatan log yang dihasilkan oleh server. Teknologi log server merupakan layanan yang biasanya ada pada sistem operasi. Teknologi ini mampu memberikan informasi catatan akses terhadap server oleh pengguna. Dalam penelitian digunakan teknologi log server untuk menelusuri data akses pengguna situs web. Informasi yang dihasilkan oleh layanan log server berupa file teks. Isi file log dikonversi menjadi bentuk data MySQL yang lebih mudah untuk diolah. Hasil penelitian, berhasil menyusun metode pemanfaatan teknologi log server untuk analisis akses web. Dari analisis data log server, ditemukan pola kunjungan pada situs. Hasil analisis berguna bagi pengembangan situs lebih lanjut.

Analisis data akses situs berdasar teknologi log server

Perkembangan teknologi informasi telah memberikan dampak yang signifikan dalam berbagai aspek kehidupan manusia, termasuk dalam pemerintahan dan program sosial seperti penanggulangan kemiskinan. Penggunaan komputer dan teknologi informasi memberikan peluang untuk meningkatkan efisiensi dan akurasi informasi. Salah satu pendekatan yang digunakan dalam program-program sosial adalah penargetan berbasis keluarga dengan kriteria yang komprehensif. Meskipun demikian, proses pendataan dalam program-program tersebut sering mengalami kendala, terutama terkait kompleksitas dan potensi kesalahan dalam pencatatan data. Penggunaan teknologi informasi, seperti aplikasi Google Forms, dianggap sebagai solusi yang efektif. Google Forms memberikan kemudahan dalam mengumpulkan data melalui formulir online dengan cepat dan akurat. Program pengabdian masyarakat dilakukan untuk meningkatkan pemahaman dan keterampilan menggunakan Google Forms, khususnya kepada Pamong dan Perangkat Kalurahan Wedomartani. Hasil program ini menunjukkan peningkatan signifikan dalam pengetahuan dan keterampilan peserta dalam menggunakan Google Forms. Respon positif juga diterima dari mitra terkait. Program ini berhasil meningkatkan penerapan teknologi informasi di tingkat pemerintahan daerah, serta membantu dalam efisiensi dan akurasi proses pendataan untuk program-program sosial, khususnya dalam penanggulangan kemiskinan.

Pendataan warga miskin di kalurahan wedomartani ngemplak sleman menggunakan google forms

Credit is the process of borrowing money from customers to be paid over a certain period of time and with a payment agreement. In general, credit is provided by companies operating in the financial sector such as banks, cooperatives, business credit and finance. In the implementation process, providing credit to customers must be appropriate. In reality, the process of granting credit is still given to the wrong people. The problems faced must be resolved immediately and well, if the problems continue and giving credit not to the right customers will be very detrimental to the company. The settlement process can be done by looking at customer data that has previously received credit. Data mining is a technique that can be used to help solve these problems. In the process of resolving credit granting problems, data mining can be used to process previous credit customer data to obtain a pattern of which customers are eligible for credit. Classification is a method used in data mining to solve various kinds of problems. In this research, research will be carried out using the Naïve Bayes algorithm and the C4.5 algorithm. The method comparison process carried out in the research was carried out to obtain more definite results. This is based on the importance of giving credit to the right person so that there are no problems in the process of completing credit bill payments. Completion of data mining by applying the Naïve Bayes and C4.5 algorithms has been successfully carried out and classification can be carried out for decision making, both algorithms have the same decision making result, namely "Accepted". However, there are differences in the level of accuracy obtained. In the Naïve Bayes algorithm the accuracy level is 86.67%, while in the C4.5 algorithm the accuracy level is 100%.

Penerapan Data Mining Untuk Klasifikasi Penerima Kredit Dengan Perbandingan Algoritma Naïve Bayes dan Algoritma C4.5

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