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Design of Expert System for Identification of Learning Modalities and Multiple Intelligences in Students with Fuzzy Logic Method Nova Fatmasari; Eva Rianti; Hari Marfalino
Journal of Computer Scine and Information Technology Volume 10 Issue 4 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i4.110

Multiple intelligences and learning modalities possessed by each student need to be considered by teachers. Because to maximize the learning process, both of these things are needed. However, this is often ignored by teachers. The learning process that is carried out only focuses on single intelligence and learning methods that focus on paying attention to the teacher explaining the material. This method certainly causes the learning process to be less than optimal. therefore , an expert system is needed that can help teachers and students know this. The expert system that will be processed takes knowledge from the Guidance and Counseling teacher of SMAN 15 Padang using the Fuzzy Logic Tsukamoto method. This expert system is processed using the Visual Basic.Net programming language, this expert system can identify 4 types of multiple intelligences, namely linguistics, mathematical logic, music and intrapersonal and also the learning modalities possessed by each student with predetermined rules. The results of the expert system can help teachers and students in improving the learning process. With this system, it can help schools, especially Guidance and Counseling teachers, in identifying the types of learning and multiple intelligences possessed by each student. So that it can develop and provide solutions for developing student abilities.

Penerapan Metode Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) dalam Penentuan Menu Terfavorit di Sero Caffe Pasrah Surya Babega; Hari Marfalino
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1053

This study discusses the application of the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method in a mobile-based Decision Support System to determine the most favorite menu at Sero Caffe. The problem faced is that the process of determining the best menu is still subjective and has not been supported by structured data analysis. This study aims to design a system that can assist management in making objective decisions based on several evaluation criteria, namely taste, price, popularity, and health. The data were obtained from customer evaluations of the available menus and then processed using the MOORA method stages, including the construction of the decision matrix, normalization, weighting, and ranking of alternatives. The results show that the developed decision support system is able to provide accurate and systematic recommendations for the most favorite menu, thereby improving the effectiveness of decision making and supporting business strategy development at Sero Caffe.

Sistem Pakar Identifikasi Hama dan Penyakit Tanaman Menggunakan Metode Certainty Factor dan Forward Chaining Firdaus; Hari Marfalino; Dinul Akhiyar
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.960

Hama dan penyakit tanaman merupakan salah satu permasalahan utama yang dapat menurunkan produktivitas dan kualitas hasil pertanian. Keterbatasan pengetahuan petani dalam mengidentifikasi jenis hama dan penyakit berdasarkan gejala yang muncul sering menyebabkan penanganan yang kurang tepat. Penelitian ini bertujuan untuk merancang dan membangun sistem pakar yang mampu mengidentifikasi hama dan penyakit tanaman berdasarkan gejala yang dialami tanaman. Sistem pakar dikembangkan menggunakan metode Forward Chaining sebagai mekanisme penelusuran aturan dan metode Certainty Factor untuk menghitung tingkat keyakinan diagnosis berdasarkan kombinasi nilai keyakinan pakar dan pengguna. Data pengetahuan diperoleh dari aturan yang menghubungkan gejala dengan jenis hama atau penyakit tertentu. Hasil pengujian menunjukkan bahwa sistem mampu memberikan diagnosis hama dan penyakit tanaman beserta tingkat kepastian dan rekomendasi solusi secara informatif. Sistem ini diharapkan dapat membantu pengguna, khususnya petani, dalam melakukan identifikasi awal hama dan penyakit tanaman secara cepat dan akurat sebagai dasar pengambilan keputusan penanganan.

Pemanfaatan Machine Learning Dalam Analisis Data Untuk Mendukung Pengambilan Keputusan Cerdas Berbasis Data Modern Dinul Akhiyar; Hari Marfalino; Radiyan Rahim
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1194

Perkembangan teknologi digital telah menghasilkan volume data yang sangat besar dan kompleks sehingga membutuhkan metode analisis yang lebih efektif dibandingkan pendekatan konvensional. Machine Learning merupakan salah satu cabang kecerdasan buatan yang mampu mengolah data dalam jumlah besar untuk menghasilkan informasi yang mendukung pengambilan keputusan secara cerdas. Penelitian ini bertujuan menganalisis pemanfaatan Machine Learning dalam proses analisis data modern serta kontribusinya terhadap peningkatan kualitas pengambilan keputusan. Metode penelitian yang digunakan adalah studi literatur dengan pendekatan deskriptif melalui pengumpulan berbagai sumber ilmiah terkait algoritma Machine Learning, analisis data, dan sistem pendukung keputusan. Hasil penelitian menunjukkan bahwa algoritma Machine Learning seperti Decision Tree, Random Forest, dan Neural Network mampu meningkatkan akurasi prediksi, mengidentifikasi pola tersembunyi, serta memberikan rekomendasi berbasis data secara otomatis. Implementasi teknologi ini telah diterapkan pada berbagai sektor seperti bisnis, kesehatan, pendidikan, dan keuangan. Dengan demikian, Machine Learning menjadi teknologi strategis yang mampu mendukung organisasi dalam menghasilkan keputusan yang lebih cepat, akurat, dan efektif di era data modern.

SISTEM INFORMASI PENJUALAN FARMASI SAUDARA MENGGUNAKAN ANDROID Fitri Firdalius; Dinda Djesmedi; Maharian Agung; Hari Marfalino
Jurnal Manajemen Teknologi Informatika Vol. 2 No. 1 (2024): Jurnal Manajemen Teknologi Informatika
Publisher : JENTIK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70038/jentik.v2i1.88

To meet the needs of Saudara Pharmacy, there will be an Android-based sales information system that uses the Waterfall method to manage the sales of medicines and other health products. Need analysis, system design, implementation, testing, and maintenance are the stages of the Waterfall method, chosen because of its systematic and sequential approach. This method makes it easy for the developer team to ensure that each stage of development is well completed before proceeding to the next stage. Information is collected about the operational needs of Saudara Pharmacy at the stage of needs analysis. This data is used to create a system that can monitor the stock of drugs, automate the sales process, and provide direct sales reports. The easy-to-use Android application to implement allows pharmacy employees to perform sales transactions quickly and accurately. Drug barcode scanning, inventory management, sales transaction recording, and the creation of daily sales reports are the main features of this app. The system is tested to ensure that the application works properly and has no errors. In addition, user feedback is collected and studied to enable improvements and features. It is expected that the Android-based Sales Information System at Saudara Pharmacy will improve operational efficiency, reduce sales recording errors, and provide accurate data for management to make decisions. Moreover, the system will increase customer satisfaction by providing faster and more accurate services.

Sistem Pakar Identifikasi Hama dan Penyakit Tanaman Menggunakan Metode Certainty Factor dan Forward Chaining Firdaus; Hari Marfalino; Dinul Akhiyar
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.960

Hama dan penyakit tanaman merupakan salah satu permasalahan utama yang dapat menurunkan produktivitas dan kualitas hasil pertanian. Keterbatasan pengetahuan petani dalam mengidentifikasi jenis hama dan penyakit berdasarkan gejala yang muncul sering menyebabkan penanganan yang kurang tepat. Penelitian ini bertujuan untuk merancang dan membangun sistem pakar yang mampu mengidentifikasi hama dan penyakit tanaman berdasarkan gejala yang dialami tanaman. Sistem pakar dikembangkan menggunakan metode Forward Chaining sebagai mekanisme penelusuran aturan dan metode Certainty Factor untuk menghitung tingkat keyakinan diagnosis berdasarkan kombinasi nilai keyakinan pakar dan pengguna. Data pengetahuan diperoleh dari aturan yang menghubungkan gejala dengan jenis hama atau penyakit tertentu. Hasil pengujian menunjukkan bahwa sistem mampu memberikan diagnosis hama dan penyakit tanaman beserta tingkat kepastian dan rekomendasi solusi secara informatif. Sistem ini diharapkan dapat membantu pengguna, khususnya petani, dalam melakukan identifikasi awal hama dan penyakit tanaman secara cepat dan akurat sebagai dasar pengambilan keputusan penanganan.

Penerapan Metode Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) dalam Penentuan Menu Terfavorit di Sero Caffe Pasrah Surya Babega; Hari Marfalino
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1053

This study discusses the application of the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method in a mobile-based Decision Support System to determine the most favorite menu at Sero Caffe. The problem faced is that the process of determining the best menu is still subjective and has not been supported by structured data analysis. This study aims to design a system that can assist management in making objective decisions based on several evaluation criteria, namely taste, price, popularity, and health. The data were obtained from customer evaluations of the available menus and then processed using the MOORA method stages, including the construction of the decision matrix, normalization, weighting, and ranking of alternatives. The results show that the developed decision support system is able to provide accurate and systematic recommendations for the most favorite menu, thereby improving the effectiveness of decision making and supporting business strategy development at Sero Caffe.

Pemanfaatan Machine Learning Dalam Analisis Data Untuk Mendukung Pengambilan Keputusan Cerdas Berbasis Data Modern Dinul Akhiyar; Hari Marfalino; Radiyan Rahim
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1194

Perkembangan teknologi digital telah menghasilkan volume data yang sangat besar dan kompleks sehingga membutuhkan metode analisis yang lebih efektif dibandingkan pendekatan konvensional. Machine Learning merupakan salah satu cabang kecerdasan buatan yang mampu mengolah data dalam jumlah besar untuk menghasilkan informasi yang mendukung pengambilan keputusan secara cerdas. Penelitian ini bertujuan menganalisis pemanfaatan Machine Learning dalam proses analisis data modern serta kontribusinya terhadap peningkatan kualitas pengambilan keputusan. Metode penelitian yang digunakan adalah studi literatur dengan pendekatan deskriptif melalui pengumpulan berbagai sumber ilmiah terkait algoritma Machine Learning, analisis data, dan sistem pendukung keputusan. Hasil penelitian menunjukkan bahwa algoritma Machine Learning seperti Decision Tree, Random Forest, dan Neural Network mampu meningkatkan akurasi prediksi, mengidentifikasi pola tersembunyi, serta memberikan rekomendasi berbasis data secara otomatis. Implementasi teknologi ini telah diterapkan pada berbagai sektor seperti bisnis, kesehatan, pendidikan, dan keuangan. Dengan demikian, Machine Learning menjadi teknologi strategis yang mampu mendukung organisasi dalam menghasilkan keputusan yang lebih cepat, akurat, dan efektif di era data modern.

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