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All Journal JUSIFO : Jurnal Sistem Informasi INOVTEK Polbeng - Seri Informatika Building of Informatics, Technology and Science Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Computer Science and Information Technology (CoSciTech)
Muttakin, Fitriani
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Computer Science & IT

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Analisa Usability SIKULI Menggunakan Metode Usability Testing Berbasis ISO 9241-11 Auliadi, Maulana; Muttakin, Fitriani; Rahmawita Munzir, Medyantiwi
TEMATIK Vol 10 No 1 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v10i1.1300

Abstract

Sistem Informasi Kuliah Online (SIKULI) merupakan website penunjang kegiatan perkuliahan yang digunakan untuk membantu dosen dan mahasiswa dalam kegiatan perkuliahan. SIKULI menjadi sistem penunjang perkuliahan berupa website e-learning namun masih terjadi beberapa kesalahan yang menyebabkan rasa tidak nyaman oleh penggunanya. Layanan ataupun fitur yang tersedia pada SIKULI meliputi halaman beranda, jadwal mata kuliah, halaman mata kuliah, detail otorisasi, capaian dan deskripsi mata kuliah, data pertemuan dan data nilai. Selama penggunaanya terkadang terjadi kendala seperti absensi tidak terbaca, Rencana Pembelajaran Semester yang tidak dapat diunduh dan tugas yang tidak dapat di-upload. Penelitian ini bertujuan untuk melihat dan menganalisa pengaruh kegunaan (usability) terhadap pengguna menggunakan metode Usability Testing berdasarkan ISO 9241-11. Tiga aspek pengukuran kegunaan (Usability) menurut IS0 9241-11 yaitu keefektifitasan (Effectiveness), efisiensi (Efficiency), dankepuasan (Satisfaction). Pengambilan data dilakukan dengan menyebarkan kuesioner penelitian terhadap pengguna website SIKULI, selanjutnya dilakukan proses analisa data dengan melakukan uji validitas dan reliabilitas data, dan dilanjutkan dengan pengujian hipotesis menggunakan SmartPLS. Hasil penelitian ini menunjukkan bahwa SIKULI sudah dikatakan cukup bagus dengan nilai keterkaitan R-Square variabel kegunaan (Usability)sebesar 66.8%.
Perbandingan Algoritma KNN, NBC, dan SVM: Analisis Sentimen Masyarakat Terhadap Perparkiran di Kota Pekanbaru Intan, Sofia Fulvi; Permana, Inggih; Salisah, Febi Nur; Afdal, M.; Muttakin, Fitriani
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): JUSIFO (Jurnal Sistem Informasi) | December 2023
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21357

Abstract

The public response in Pekanbaru to parking policies and regulations has given rise to various sentiments, both positive and negative. This discussion extends not only within the local community but also across various social media platforms. This research aims to analyze public sentiment towards the new parking policies and regulations in the Pekanbaru area. The study involves the KNN, NBC, and SVM algorithms to classify public sentiment into positive, neutral, and negative categories. Balancing techniques used in this research include Random Over Sampling (ROS) and Random Under Sampling (RUS). The data utilized in this study were obtained from posts on the social media platform X. The testing of the dataset using ROS resulted in high accuracy, precision, and recall values. The findings of this research indicate that overall, the SVM algorithm outperforms KNN and NBC in terms of accuracy, precision, and recall. Additionally, the most dominant sentiment is negative, with 422 tweets expressing dissatisfaction with the current parking policies.
A Comparative Study of the Performance of KNN, NBC, C4.5, and Random Forest Algorithms in Classifying Beneficiaries of the Kartu Indonesia Sehat Program Nabillah, Putri; Permana, Inggih; Afdal, M.; Muttakin, Fitriani; Marsal, Arif
JUSIFO : Jurnal Sistem Informasi Vol 10 No 1 (2024): JUSIFO (Jurnal Sistem Informasi) | June 2024
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v10i1.21536

Abstract

This study evaluates the performance of various algorithms in determining eligible recipients for the Kartu Indonesia Sehat program. The Random Forest algorithm demonstrated the highest accuracy, precision, and recall, with values of 72.08%, 72.41%, and 99.64%, respectively. The emphasis on recall helps minimize errors in identifying eligible recipients. Additionally, the C4.5 algorithm reduced the total number of variables from 33 to 8, highlighting its computational efficiency. The findings provide valuable insights for the Social Affairs Office of Dumai City in making informed decisions regarding KIS eligibility. The results underscore the effectiveness of using algorithmic approaches to enhance the accuracy and efficiency of aid distribution processes.
Klasifikasi Penerima Bantuan Beras Miskin Menggunakan Algoritma K-NN, NBC dan C4.5 Pristiawati, Andani Putri; Permana, Inggih; Zarnelly, Zarnelly; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3617

Abstract

One of the tasks of the Dumai City Social Service is to provide poor rice assistance to people in need. The problem that often occurs in the distribution of rice to the poor is that the target recipients of poor rice often occur. In overcoming the existing problems, this research has carried out classification models using the K-Nearest Neighbor (K-NN) algorithm, Naïve Bayes Classifier (NBC), and C4.5 Algorithm. Based on the experimental results, it was found that the best classification model was produced by the K-NN Algorithm with a value of K equal to 21. Besides that, the C4.5 algorithm succeeded in making a decision tree for the classification model with the lowest complexity because it succeeded in reducing the number of attributes from 33 to 5 attributes. The decision tree can be used as material for consideration to the Social Service in making decisions on Raskin beneficiaries.
Sentimen Analisis Pada Ulasan Aplikasi Ajaib Di Google Play Store Dengan Algoritma Support Vector Machine Syahri, Alfi; Angraini, Angraini; Muttakin, fitriani
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4047

Abstract

Perkembangan aplikasi mobile di bidang keuangan telah memberikan kemudahan dalam berinvestasi. Salah satu aplikasi yang bisa melakukan investasi adalah aplikasi Ajaib. Dalam menggunakan aplikasi, Anda dapat melihat rating dan review yang diberikan oleh pengguna di platform Google Play Store. Ulasan pengguna pada Aplikasi Ajaib memberikan gambaran penting bagi calon pengguna dalam memahami kualitas dan kepuasan pengguna. Namun, banyaknya tinjauan membuat analisis manual menjadi sulit dan tidak efisien. Oleh karena itu diperlukan suatu teknik klasifikasi review yang memanfaatkan algoritma Support Vector Machine (SVM). Implementasinya dilakukan melalui bahasa pemrograman Python. Teknik Support Vector Machine menunjukkan akurasi luar biasa dalam menangani data berdimensi tinggi dan data tidak seimbang. Tujuan dari penelitian ini adalah untuk memfasilitasi kemajuan Aplikasi Ajaib dengan memanfaatkan umpan balik yang diberikan, mengatasi keluhan pelanggan, dan meningkatkan pengalaman pengguna secara keseluruhan. Data yang digunakan diambil dari review aplikasi sebanyak 5000 data dengan rating yang bervariasi pada bulan Januari hingga Oktober 2023. Berdasarkan hasil penelitian diperoleh akurasi sebesar 87,57%. Pada kelas positif diperoleh presisi 93%, recall 97%, dan skor f-1 95%. Sedangkan kelas netral memperoleh presisi sebesar 75%, recall 53%, dan skor f-1 sebesar 62%. Serta pada kelas negatif mendapatkan presisi sebesar 75%, recall sebesar 87%, dan skor f-1 sebesar 80%.
Optimalisasi Pembobotan Metode MABAC Menggunakan Entropy untuk Pemilihan Penerima BLT Dana Desa Muttakin, Fitriani; Rahmawati, Rahmawati; Safrudin, Muhammad
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6426

Abstract

Improving social welfare through the provision of Direct Cash Assistance for village funds is one of the government's efforts to support the rural economy. In the distribution process of this assistance, precise coordination and analysis are necessary to ensure that each program is targeted accurately without any elements of misappropriation. Direct Cash Assistance for village funds has several criteria that must be met by potential recipients, necessitating a multi-criteria decision support method to determine candidates who meet these criteria at the most appropriate level. In addressing various challenges such as inaccurate targeting and lack of transparency in fund distribution, time-consuming data processes, and the neglect of differences in criteria values can slow down the decision-making for aid recipients. In this research, the use of the Entropy Method for optimizing criteria weighting in problem resolution is significant. The obtained weights are then integrated with the Multi-Attributive Border Approximation area Comparison (MABAC) method, aiming to determine the ranking of aid recipients most suitable. Finally, the ranking results are interpreted as identifying the candidates most fitting to receive direct cash assistance for village funds.
Co-Authors Angraini Angraini Arif Marsal Auliadi, Maulana Febi Nur Salisah, Febi Nur Inggih Permana Intan, Sofia Fulvi M. Afdal Nabillah, Putri Pristiawati, Andani Putri Rahmawati, Rahmawati Rahmawita Munzir, Medyantiwi Safrudin, Muhammad Syahri, Alfi Zarnelly Zarnelly