Jurnal Riset Sistem dan Teknologi Informasi

BUILDING DISTRIBUTED APPLICATIONS TO CALCULATE COURSE LEARNING OUTCOMES

Joko Triyono — 2024-02-01

Information technology has become an inseparable part of everyday life, as well as in the professional activities of lecturers. In basic concepts, information will follow a pattern starting from input – process and output, so that the quality of information will depend on the quality of incoming input. In the field of academic activities, the relationship between data is very much, and each of these data binds each other and influences each other. With the complexity of activities in the academic field and various problems that arise, so that activities in the academic field appear many information systems that are sometimes not related between each information system. Specific cases from sections in the academic field, for example, in analyzing the results of learning activities that are always carried out at the end of each semester in the form of course learning outcomes, lecturers will always carry out these activities at the end of each semester, with data sources in the form of curriculum, RPS and student grades, lecturers will calculate to produce information on the results of learning activities. The result of this study is a web application architecture design model so that a distributed application can be used by departments and lecturers to calculate the learning outcomes of courses that are simpler and more effective

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI CABANG MINIMARKET TERBAIK MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING BERBASIS WEB

Muqorobin Muqorobin — 2024-02-02

Minimarkets are shops that sell daily necessities. This mini market is located on Jalan Station Kauman, Krikilan Hamlet, Dawungan Village, Masaran District, Sragen Regency, Central Java Province. In developing inter-company leaders, it is difficult to make decisions about the location of new branches, because there are many criteria such as: strategic location, distance and population to facilitate decision making. The purpose of this study is that researchers assist company leaders in choosing the best new minimarket branch locations using the SAW algorithm method. This method was chosen because it is able to carry out the process of ranking and weighting the best alternatives by applying many criteria. The technique used in this research is observation (observation), interview (interview), and literature study. In the design of this system is made with Context Diagram, HIPO, DAD, relations between tables and database design. This application is made using the PHP programming language and the database uses MySQL. The final result is a report on the best location data. System testing is done by testing the functionality and testing the validity of the obtained results are 100% valid.

PENERAPAN METODE SUPPORT VECTOR MACHINE (SVM) PADA KLASIFIKASI JENIS CENGKEH BERDASARKAN FITUR TEKSTUR DAUN

Sadri Talib — 2024-02-02

Leaves are a very important plant component because they play an important role in differentiating plant species, including clove plants. Currently, the identification of clove species, namely Afo, Siputih, and Zanzibar, relies on manual observation of the characteristics of the fruit and flowers, which can take a long time, especially considering the long fruiting period of the clove plant. To answer this problem, the authors conducted a study to classify the three types of clove leaves based on the characteristics and texture of the Gray gray-level co-occurrence Matrix (GLCM), which includes four parameters: Contrast, Correlation, Energy, and Homogeneity.

The Support Vector Machine (SVM) classification algorithm processes extracted feature values and accurately class leaves. This study achieves the highest accuracy of 56.67% on an image size of 250x250 pixels and 48.33% on an image size of 150x150 pixels using 150 training data and 60 test data. These results indicate the potential of automatic leaf classification in efficiently identifying clove plant species.

Keywords : Clove, Leaf, Processing, Texture, SVM

DECISION SUPPORT SYSTEM IN IMPROVING THE QUALITY OF BANJARMASIN TOURISM, GET TOUR APPLICATION USING THE SAW METHOD

Kamarudin . — 2024-02-02

Tourism is an activity that is liked by many people, even tourism is one of the important needs, especially regarding socio-economic activities which are seen as having good prospects in the future. In South Kalimantan, especially the city of Banjarmasin, there are many good tourist attractions such as the historical mosque of Sultan Suriansyah, Siring Park, Banjarmaisn City, and culinary tours of Arab villages. Of the tourist attractions that have been mentioned, tourists are still confused in determining tourist attractions because there are many places and a lack of information about tourist attractions in South Kalimantan. From this description, a Decision Support System (DSS) or Decision Support System (DSS) was created to determine tourist attractions in South Kalimantan called the Get Tour application. In addition to displaying information about tourist attractions, this application also displays information on tourist attractions in the form of a map. The results of calculations in the application are in accordance with the formula and expected results based on several criteria, namely distance, parking area, the first special criteria, the second special criteria and the third special criteria using the Simple Additive Weighting (SAW) method.

Optimisasi Kesuksesan Akademis: Studi Sistem Deteksi Dini untuk Mengidentifikasi Potensi Dropout Mahasiswa Tingkat Akhir dengan Protokol Intervensi Cepat pada Perguruan Tinggi

Ismail Setiawan — 2024-02-03

Penelitian ini bertujuan mengoptimalkan kesuksesan akademis dengan mengembangkan sistem deteksi dini guna mengidentifikasi potensi mahasiswa tingkat akhir yang berisiko dropout. Penelitian berfokus pada perancangan sistem yang mampu memberikan peringatan kepada pembimbing akademik atau bagian akademik kampus untuk segera melakukan mitigasi pencegahan, dengan tujuan mengurangi potensi mahasiswa drop out lebih banyak.

Metodologi penelitian dimulai dengan analisis faktor dropout melibatkan tinjauan literatur, wawancara, dan studi kasus. Selanjutnya, model prediktif dikembangkan menggunakan metode machine learning, seperti regresi logistik, dengan memanfaatkan data akademik dan perilaku studi mahasiswa. Implementasi sistem deteksi dilakukan dengan menyusun antarmuka pengguna yang user-friendly dan integrasi yang baik dengan sistem informasi kampus.

Hasil penelitian ini menunjukkan bahwa model prediktif yang dikembangkan mampu mengidentifikasi mahasiswa tingkat akhir yang berpotensi dropout dengan akurasi yang tinggi. Sistem deteksi memberikan peringatan dini kepada pembimbing akademik atau bagian akademik kampus, memungkinkan mereka untuk mengambil langkah-langkah pencegahan dan memberikan bantuan tepat waktu. Evaluasi kinerja sistem menunjukkan efektivitasnya dalam meningkatkan kesadaran dan respon terhadap potensi mahasiswa drop out, dengan harapan dapat memberikan kontribusi positif terhadap peningkatan tingkat kelulusan dan kualitas pendidikan tinggi secara keseluruhan.