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All Journal JURNAL SISTEM INFORMASI BISNIS REiD (Research and Evaluation in Education) Jurnal Ilmiah FIFO JSAI (Journal Scientific and Applied Informatics) Jurnal Pengabdian Masyarakat Nasional
Sulis Sandiwarno
Universitas Mercu Buana
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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PERANCANGAN MODEL E-LEARNING BERBASIS COLLABORATIVE VIDEO CONFERENCE LEARNING GUNA MENDAPATKAN HASIL PEMBELAJARAN YANG EFEKTIF DAN EFISIEN Sulis Sandiwarno
Jurnal Ilmiah FIFO Vol 8, No 2 (2016)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemanfaatan Teknologi Informasi saat ini sangatlah berkembang dengan pesat, dengan adanya Teknologi Informasi ini dapat membantu sseluruh aktifitas dan dapat menghasilkan laporan dengan cepat dan baik. Pemanfaatan Teknologi Informasi ini berperan penting juga dalam tingkat pendidikan, seperti yang kita ketahui adalah e-learning. Dengan adanya e-learning seluruh aktifitas pembelajaran dapat mudah untuk dilakukan. Dalam penelitian ini akan membahas mengenai e-learning dengan harapan dan tujuan seluruh proses pembelajaran dapat dengan mudah dilakukan oleh pengajar maupun siswa yang berada dalam lingkup proses pembelajaran. Metode yang akan dilakukan untuk mengukur tingkat kepuasaan penggunaan e-learning ini adalah dengan dengan menggunakan Collaborative Learning berbasis TAM (Technology Acceptance Model) dan Naives Bayes Classification (NBC). Diharapkan dengan adanya perancangan teknologi e-learning berbasis video conference ini seluruh proses pembelajaran menjadi efektif dan efisien
Empirical lecturers’ and students’ satisfaction assessment in e-learning systems based on the usage metrics Sulis Sandiwarno
REID (Research and Evaluation in Education) Vol 7, No 2 (2021)
Publisher : Sekolah Pascasarjana Universitas Negeri Yogyakarta & HEPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/reid.v7i2.39642

Abstract

Nowadays, in the pandemic of COVID-19, e-learning systems have been widely used to facilitate teaching and learning processes between lecturers and students. Assessing lecturers’ and students’ satisfaction with e-learning systems has become essential in improving the quality of education for higher learning institutions. Most existing approaches have attempted to assess users’ satisfaction based on System Usability Scale (SUS). On the other hand, different studies proposed usage-based metrics (completion rate, task duration, and mouse or cursor distance) which assess users’ satisfaction based on how they use and interact with the system. However, the cursor or mouse distance metric does not consider the effectiveness of navigation in e-learning systems, and such approaches measure either lecturers’ or students’ satisfaction independently. Towards this end, we propose a lostness metric to replace the click or cursor distance metric for assessing lecturers’ and students’ satisfaction with using e-learning systems. Furthermore, to obtain a deep analysis of users’ satisfaction, we tandem the usage-based metric (i.e., completion rate, task duration, and lostness) and the SUS metric. The evaluation results indicate that the proposed approach can precisely predict users’ satisfaction with e-learning systems.
Model Sequential Resnet50 Untuk Pengenalan Tulisan Tangan Aksara Arab Sarwati Rahayu; Sulis Sandiwarno; Erwin Dwika Putra; Marissa Utami; Hadiguna Setiawan
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 2 (2023): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i2.5379

Abstract

Research for Arabic handwriting recognition is still limited. The number of public datasets regarding Arabic script is still limited for this type of public dataset. Therefore, each study usually uses its dataset to conduct research. However, recently public datasets have become available and become research opportunities to compare methods with the same dataset. This study aimed to determine the implementation of the transfer learning model with the best accuracy for handwriting recognition in Arabic script. The results of the experiment using ResNet50 are as follows: training accuracy is 91.63%, validation accuracy is 91.82%, and the testing accuracy is 95.03%.
Komparasi Hasil Color Feature Extraction HSV, LAB dan YCrCb pda Algoritma SVM untuk Klasifikasi Spesies Burung Sarwati Rahayu; Andi Nugroho; Erwin Dwika Putra; Mariana Purba; Hadiguna Setiawan; Sulis Sandiwarno
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.5920

Abstract

The classification of bird species is a problem often faced by ornithologists, and has been considered scientific research since antiquity. This study aims to evaluate the results of color feature extraction including HSV, LAB and YCrCb against the results of the SVM classification. In addition, the results of this study are useful to determine the performance of color feature extraction that is suitable for bird species classification. The dataset used was 22,617 bird species images. Based on experimental results, the effect of HSV on the SVM classification caused a decrease in accuracy by -0.33% while LAB and YCrCb on the SVM classification caused an increase in accuracy of 0.44% and 0.21%. However, the accuracy of the SVM classification does not yet have good performance so that further research will be carried out using other classifications, including convolutional neural networks and others.
Penerapan Machine Learning Untuk Prediksi Bencana Banjir Sulis Sandiwarno
Jurnal Sistem Informasi Bisnis Vol 14, No 1 (2024): Volume 14 Nomor 1 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss1pp62-76

Abstract

Indonesia beriklim tropis karena terletak pada garis katulistiwa, oleh karena itu Indonesia juga hanya memiliki dua musim, yaitu musim kemarau dan musim hujan. Apabila musim hujan tiba dan curah hujan intensitasnya tinggi, maka hal tersebut berpotensi menyebabkan bencana banjir. Kerugian yang ditimbulkan akibat bencana banjir cukup besar. Untuk mengurangi risiko bencana dan kerugian akibat banjir, diperlukan inovasi dalam mitigasi bencana. Beberapa penelitan sebelumnya telah melakukan analisa dan prediksi mengenai bencana banjir dengan menggunakan metode berbasis machine learning seperti Support Vector Machine (SVM), K-Nearest Neighbor (KNN), dan Naive Bayes. Akan tetapi metode yang digunakan dalam penelitian tersebut memiliki permasalahan seperti tingkat akurasi yang rendah dan membutuhkan waktu yang lama untuk melakukan perhitungan data. Dalam penelitian ini kami mengusulkan sebuah model baru yang dinamakan Deep Neural Investigation Network (DNIN) algorithm, yang dikombinasikan dari Convolutional Neural Network (CNN) dan Bidirectional Long Short Term Memory (BiLSTM). Proses dari usulan metode dalam penelitiaan terdiri dari tiga bagian, yang pertama Convolutional Neural Network (CNN) digunakan untuk melakukan ekstraksi fitur spasial dari data banjir, selanjutnya Bidirectional Long Short Term Memory (BiLSTM) digunakan untuk menangkap pola temporal dari fitur-fitur tersebut. Kemudian tahap terakhir adalah menggabungkan hasil dari kedua metode tersebut. Hasil dari penelitian yang dilakukan terhadap data curah hujan, didapatkan informasi bahwa model yang kami usulkan lebih unggul dibandingkan dengan model sebelumnya dalam melakukan prediksi bencana banjir.
Penerapan Metode Service Quality (SERVQUAL) dan Simple Additive Weighting (SAW) untuk Menentukan Pengambilan Keputusan terhadap Kepuasan Pelanggan (Studi Kasus: Restoran Ayam Geprek) Sulis Sandiwarno
Jurnal Sistem Informasi Bisnis Vol 14, No 1 (2024): Volume 14 Nomor 1 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss1pp88-99

Abstract

Perkembangan usaha kuliner di Jakarta bisa dibilang mengalami perkembangan yang cukup pesat. Setiap bulannya selalu ada resto baru maupun tempat makan baru seperti pedagang kaki lima maupun kafe. Untuk menghadapi persaingan tersebut, perlu adanya suatu ulasan kepada para pelaku Usaha Mikro Kecil dan Menengah (UMKM) agar dapat melakukan peningkatan dan perbaikan dalam pelayanannya. Pada penelitian terdahulu, telah dilakukan analisis terhadap kepuasan pelanggan menggunakan metode Simple Additive Weighting (SAW). SAW merupakan suatu metode penjumlahan terbobot yang dikenal secara luas untuk pengambilan keputusan. Akan tetapi, metode tersebut memiliki masalah seperti kurangnya akurasi data. Untuk mengatasi permasalahan tersebut dalam penelitian ini kami ingin menggunakan metode Servqual secara bersama di metode SAW. Tahapan dari usulan penelitian yang kami lakukan adalah melakukan pengumpulan data menggunakan kuesioner yang disebar lalu dianalisis menggunakan metode Servqual dan SAW. Berdasarkan uji coba sistem yang dilakukan pada 5 cabang restoran ayam geprek Sultan, didapatkan hasil bahwa pemilik harus memperbaiki dimensi Responsiveness dengan nilai gap sebesar 0.105, nilai ini lebih rendah dibandingkan dimensi yang lain. Sedangkan cabang Semper ini memiliki prioritas terbesar untuk dilakukan perbaikan dibandingkan dengan 4 cabang lainnya dengan nilai akhir 0.6237. Dengan hasil yang didapatkan tersebut, diharapkan pemilik restoran dapat memberikan perbaikan pelayanannya.
Analisis Performa Metode Klasifikasi Dataset Multi-Class Kanker Kulit Menggunakan KNN dan HOG Rahayu, Sarwati; Sandiwarno, Sulis; Dwika Putra, Erwin; Utami, Marissa; Setiawan, Hadiguna
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6423

Abstract

Detection of skin cancer in its early phase is a challenge even for dermatologists. This study aims to analyze the performance of classification methods on multiclass skin cancer datasets using K-nearest neighbor (KNN) and histogram of oriented gradients (HOG). The dataset is taken publicly under the name Skin Cancer MNIST dataset: HAM10000 dataset totaling 10,015 data. The first experiment used the pixels per cell parameter of 8.8 and cells per block of 2.2 to get an accuracy of 60.58%. The second experiment used the pixels per cell parameter of 8.8 and cells per block of 2.2 to get an accuracy of 60.58%. The last experiment using the pixels per cell parameter of 8.8 and cells per block of 2.2 got the best accuracy of 61.43%.
Analisis Usabilitas Sistem Informasi Akademik Berdasarkan Usability Scale (Studi Kasus: Universitas Mercu Buana) Rahayu, Sarwati; Nugroho, Andi; Sandiwarno, Sulis; Salamah, Umniy; Dwika Putra, Erwin; Purba, Mariana; Setiawan, Hadiguna
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7478

Abstract

The usability analysis on the website of Mercu Buana University (UMB) is an important research carried out to ensure that the site effectively supports the university's goals, especially in terms of the user's experience in completing academic and administrative goals with ethical and professional standards. This research was carried out during the period January 2024 to May 2024. The main purpose of this study is to measure the usability of the UMB website using a questionnaire method. The questionnaire used for the research adapted the System Usability Scale (SUS) which consisted of a total of 10 questions. Based on the calculation of each statement item having a minimum score of 0 and a maximum score of 2.5, the final score of each respondent ranged from 0 to l00. The average score obtained was 63,125. Based on the results of the score of 63,125, the UMB website has a score in the range of 50 to 70. This shows that the UMB website is in the "quite good" category but there is still a need for a little improvement. Some icons or layouts on the UMB website are not familiar to respondents. In addition, there needs to be guidelines developed to provide information on how to use the website for users who are using the UMB website for the first time.
Penentuan Prioritas Persediaan Barang dengan Menggunakan Hybrid Method Aldino, Muhammad Satria; Sandiwarno, Sulis
Jurnal Sistem Informasi Bisnis Vol 15, No 1 (2025): Volume 15 Number 1 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss1pp1-10

Abstract

Warehouse is a facility that serves as storage of goods or products. Inventory of goods has an impact on the continuity of the construction project, because if the material runs out, the contractor cannot carry out the work, as a result the project may be delayed from the predetermined schedule. The purpose of the warehouse is to monitor and control the incoming or outgoing materials in a project. In previous studies, an analysis of the AHP and TOPSIS methods has been carried out, but AHP has problems when used in cases with a large number of criteria and alternatives. While TOPSIS has problems in determining the value of the criteria because it is too subjective. Therefore, in this study we propose a hybrid method for calculating DSS which is called “Analytical Hierarchy – Similarity to Ideal Process” (AH-SIP). This proposed method has goals, namely in determining the value of the criteria with a comparison matrix using AHP, and performing alternative rankings using TOPSIS. The results of this study in determining the best material recommendations for procurement are D 25 Threaded Iron with a preference of 0.777, Chicken Wire with a preference of 0.677, and Pilox with a preference of 0.669.
Prediction Analysis of Sleep Disorders Using Machine Learning-Based Techniques Setiawati, Mega; Aldianto, Denise; Sandiwarno, Sulis
Jurnal Sistem Informasi Bisnis Vol 15, No 1 (2025): Volume 15 Number 1 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss1pp89-101

Abstract

Sleep is crucial indicator for an individual. Poor sleep quality has serious implication for health. This condition is often triggered by high work pressure and imbalance between work and rest time. While previous research with similar topic has been conducted, it has not comprehensively elucidated the key factors influencing sleep disorders. Therefore, this study conducts more in-depth analysis of factors contributing to sleep disorders including; gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress level, BMI, heart rate, and daily steps. Subsequently, we employ Machine Learning (ML) techniques to investigate further sleep disorders. The ML models include: Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Convolutional Neural Network (CNN), dan Long Short-Term Memory Network (LSTM). The objective is to assess the effectiveness of ML model implementation based on information from data and the significance of specific factors in predicting sleep disturbances. The results of this study indicate that the combination of the LR model with Chi-Square achieved the highest average F1 score, which was 84.75%, in sleep disorder classification. The research comprises several stages: (1) Data collection, (2) Pre-processing of the collected data, and (3) Training models capable of processing data for evaluation to understand the contribution of indicators to sleep disorder predictions. The findings of this study provide insights into the effectiveness of the constructed models in predicting sleep disorders
Co-Authors Aldianto, Denise Aldino, Muhammad Satria Andi Nugroho Andi Nugroho, Andi Cahyani, Lathifa Alya Fajar Masya Hadiguna Setiawan Hadiguna Setiawan Khoiri, Syakur Mariana Purba Purba, Mariana Putra, Erwin Dwika Putri, Shakira Angelina Ika Salamah, Umniy Sarwati Rahayu Setiawan, Hadiguna Setiawati, Mega