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All Journal Jurnal Teknologi dan Manajemen Informatika International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Computer Science and Information Technologies Indonesian Journal of Data and Science Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Buletin Sistem Informasi dan Teknologi Islam The Indonesian Journal of Computer Science
Darwis, Herdianti
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics

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Metode Double Exponential Smoothing pada Sistem Peramalan Tingkat Kemiskinan Kabupaten Pangkep Atussaliha, Nur Almar'; Purnawansyah, Purnawansyah; Darwis, Herdianti
ILKOM Jurnal Ilmiah Vol 12, No 3 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i3.607.183-190

Abstract

Peramalan adalah kegiatan memperkirakan kejadian yang akan terjadi berdasarkan historical data kuantitatif suatu kejadian. Peramalan sering digunakan oleh pemerintah dalam membuat suatu kebijakan. Salah satu kebijakan pemerintah adalah menurunkan angka kemiskinan setiap tahunnya. Penelitian ini bertujuan untuk membangun sistem Peramalan Tingkat Kemiskinan Kabupaten Pangkep berbasis desktop untuk memberikan gambaran jumlah tingkat kemiskinan periode selanjutnya. Dalam penelitian ini, metode peramalan yang digunakan adalah Double Exponential Smoothing dengan nilai alpha 0.001, 0.01, 0.2, 0.3, 0.5, 0.7, 0.8, 0.99, dan 0.999. Dengan menggunakan data angka kemiskinan dari tahun 2010 sampai 2019, diperoleh bahwa dari 9 nilai alpha yang digunakan, tingkat kesalahan terkecil yaitu 1.2% diberikan oleh alpha 0.5 yang diukur menggunakan metode Mean Absolute Percentage Error (MAPE). Adapun tingkat akurasi peramalan yang didapatkan jumlah kesalahan tiap alpha sebesar 95.394%.
EVALUASI KEBERGUNAAN PLATFORM PEMBELAJARAN DIGITAL SEKOLAH AL-FITYAN MENGGUNAKAN METODE SYSTEM USABILITY SCALE Magfirah, Magfirah; Hayati, Lilis Nur; Darwis, Herdianti
IDEALIS : InDonEsiA journaL Information System Vol 7 No 2 (2024): Jurnal IDEALIS Juli 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v7i2.3151

Abstract

LMS AFDAL is a learning system that is applied within the scope of SMPIT Al-Fityan School Gowa. This LMS is used to facilitate modern learning. Evaluation of the AFDAL LMS is the first step to assess whether the LMS is well received or not by users. There are many approaches that can be taken in evaluating, one of which is usability evaluation. This study aims to determine the level of usability based on the System Usability Scale method with five variables, namely learnability, efficiency, memorability, errors, and satisfaction with 10 statements as a measure of quality in terms of the usability of the LMS. This research was conducted by distributing questionnaires using google form to 306 respondents consisting of teachers and students via whatsapp. Data processing uses IBM SPSS V26 and Microsoft Excel 2019. The results of the validity and reliability tests are declared valid and reliable. The results of the SUS test show that the final SUS value of 306 respondents' responses is 64.6, according to the rules of SUS interpretation that the score is 64.6 for the Acceptability Ranges level, namely Marginal (quite acceptable), the Grade Scale results in terms of user acceptance levels are included in the C- level, and Adjectives The rating is included in the OK category. These results indicate that the AFDAL LMS is quite accepted by its users, but this figure is quite low so that some improvements are needed to make it even better.
DIET Classifier Model Analysis for Words Prediction in Academic Chatbot Astuti, Wistiani; Wibawa, Aji Prasetya; Haviluddin, Haviluddin; Darwis, Herdianti
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1598.59-67

Abstract

One prevalent conversational system within the realm of natural language processing (NLP) is chatbots, designed to facilitate interactions between humans and machines. This study focuses on predicting frequently asked questions by students using the Duel Intent and Entity Transformer (DIET) Classifier method and assessing the performance of this method. The research involves employing 300 epochs with an 80% training data and 20% testing data split. In this study, the DIET Classifier adopts a multi-task transformer architecture to simultaneously handle classification and entity recognition tasks. Notably, it possesses the capability to integrate diverse word embeddings, such as BERT and GloVe, or pre-trained words from language models, and blend them with sparse words and n-gram character-level features in a plug-and-play manner. Throughout the training process of the DIET Classifier model, data loss and accuracy from both training and testing datasets are monitored at each epoch. The evaluation of the text classification model utilizes a confusion matrix. The accuracy results for testing the DIET Classifier method are presented through four case studies, each comprising 25 text messages and 15 corresponding chatbot responses. The obtained accuracy values range from 0.488 to 0.551, F1-Score values range from 0.427 to 0.463, and precision range from 0.417 to 0.457.
Analisis Eksplorasi Data Aplikasi Android pada Playstore Munaf, Adryan Dwiprawira; Purnawansyah, Purnawansyah; Darwis, Herdianti
Buletin Sistem Informasi dan Teknologi Islam Vol 4, No 4 (2023)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v4i4.1847

Abstract

Google Playstore memiliki karakteristik yang berbeda dengan Apple App Store yaitu lebih terbuka terhadap developer aplikasi mobile sehingga memiliki varian yang lebih beragam dibanding dengan Apple App Store. Setiap aplikasi di dalam app store dapat dikelompokan berdasarkan karakteristik yang sama dan dapat disebut sebagai kategori dan genre. Pada tahun 2018 jumlah mobile app yang tersedia mencapai 3,6 juta aplikasi. Berbagai jenis mobile app tersedia pada layanan google play store, mulai dari hiburan, media sosial, editor, jasa transportasi, perdagangan (marketplace), dan kesehatan. Penelitian ini bertujuan untuk melakukan 5 analisis yaitu aplikasi dengan rating tertinggi, mencari 5 aplikasi dengan size paling berat (MBs), visualisasi data content ratings aplikasi, mengidentifikasi aplikasi dengan install terbanyak, visualisasi kategori aplikasi. Dari 2152981 data yang telah di crawling diperoleh bahwa 5 aplikasi dengan rating tertinggi yaitu Biliyor Musun - Sonsuz Yarış, CoronaSurveys, Amkshoproom Shopping, Merlin CRM, Tictactoe Superpowers dan free game. Fun and Chalmo, mencari 5 aplikasi dengan size paling berat (MBs) yaitu SkySafari 6 Pro, Audio Book Bible Offline Arabic, Audio Book Bible Offline Burmese, Audio Book Bible Offline Amharic dan Audio Book Bible Offline Germany, Visualisasi content data rating dari grafik dapat kita lihat bahwa mayoritas aplikasi mobile pada android mengatur content rating kedalam kategori Everyone, Mengindentifikasi aplikasi install terbanyak dari data yang telah diperoleh bahwa hanya terdapat 1 aplikasi yang memiliki jumlah install lebih dari 10M install dan 14 aplikasi yang memiliki jumlah install lebih dari 5 M, visualisasi kategori aplikasi dari data yang diperoleh bahwa aplikasi berkategori education memiliki jumlah terbanyak yang ada di pasar playstore saat ini.
ANALISIS SENTIMEN MAHASISWA TENTANG MODEL PERKULIAHAN HYBRID TEACHING PADA FAKULTAS ILMU KOMPUTER UMI MENGGUNAKAN MACHINE LEARNING Basri, Nur Anisa; Salim, Yulita; Darwis, Herdianti
Buletin Sistem Informasi dan Teknologi Islam Vol 5, No 2 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i2.2334

Abstract

Model perkuliahan hybrid teaching yang dilaksanakan menuai kontroversi di kalangan mahasiswa. Banyak pendapat mahasiswa yang dikeluarkan  terkait metode pembelajaran hybrid teaching di Fakultas Ilmu Komputer UMI. Penelitian ini bertujuan menganalisis sentimen mahasiswa terkait perkuliahan hybrid teaching dengan menggunakan algoritma K-Nearest Neighbor (KNN), Naïve Bayes, dan Support Vector Machine (SVM) menggunakan pelabelan NLTK, pengujian dengan  cross validation, dan menggunakan unigram tokenizing. Teknik pelabelan NLTK yang digunakan pada penelitian ini menghasilkan tingkat keakuratan algoritma KNN dengan 67.74% dibandingkan dengan algoritma Naïve Bayes dan SVM yang memiliki nilai akurasi sebesar 100%. Sehingga algoritma Naïve Bayes Classifier dan SVM dapat digunakan dengan baik pada pengklasifikasian sentimen mahasiswa terhadap perkuliahan dengan metode pembelajaran hybrid teaching di Fakultas Ilmu Komputer UMI.
Naive Bayes Classifier dan K-Nearest Neighbor pada Analisis Sentimen Perkuliahan Daring di Universitas Muslim Indonesia Wati, Silmi Nur Zaskia; Herman, Herman; Darwis, Herdianti
Buletin Sistem Informasi dan Teknologi Islam Vol 5, No 1 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i1.2202

Abstract

Pelaksanaan perkuliahan daring menggunakan KALAM di UMI banyak menuai kontroversi dikalangan mahasiswa. Banyak pendapat mahasiswa yang dikeluarkan terkait metode pembelajaran daring di UMI. Penelitian ini bertujuan menganalisis sentimen mahasiswa terkait perkuliahan daring di UMI dengan menggunakan algoritma Naïve Bayes Classifier dan K-Nearest Neighbor, serta menggabungkan berbagai metode seperti pelabelan dengan NLTK, pengujian dengan 5 cross validation, dan menggunakan unigram tokenizing. Beberapa teknik pelabelan digunakan pada penelitian ini dan menghasilkan tingkat keakuratan paling tinggi adalah pelabelan menggunakan NLTK dengan algoritma KNN dengan menggunakan SMOTE menghasilkan akurasi sebesar 100% dibandingkan dengan algoritma Naïve Bayes Classifier yang memiliki nilai akurasi sebesar 98.33%. Sehingga algoritma KNN dapat digunakan dengan baik pada pengklasifikasian sentimen mahasiswa terhadap perkuliahan daring di UMI.
Implementasi Algoritma Neural Network Untuk Memprediksi Harga Bawang Merah Di Kabupaten Bima wahidah, Nur; Lokapitasari, Poetri Lestari; Darwis, Herdianti
Buletin Sistem Informasi dan Teknologi Islam Vol 4, No 2 (2023)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v4i2.1661

Abstract

Bawang merah merupakan tanaman holtikultura yang berpotensi tinggi terhadap perubahan harga sehingga sangat fluktuatif bagi petani maupun konsumen dan juga termasuk komoditas trategis. Di Indonesia khusunya, pertumbuhan bawang merah mengikuti pola musim yang terjadi, sehingga pada musim tertentu stok bawang merah menurun. Prediksi harga bawang merah menjadi penting dilakukan untuk mengetahui harga bawang merah ke depan. Neural network termasuk algoritma yang terbaik dalam melakukan prediksi. Masalah utama bagaimana menentukan jumlah neuron dan hidden layer yang optimal sehingga akurasi prediksinya tinggi. Jurnal ini bertujuan untuk merancang arsitektur neural network dengan menggunakan algoritma bacpropagation. Tahapan penelitian dilakukan adalah mengumpulkan data harga bawang merah, melakukan proprecessing data, memproses prediksi, pengujian akurasi, pengujian akurasi dan eror serta implementasi. Dalam memproses prediksi dilakukan sesuai dengan rancangan model prediksi, yaitu parameter epoch, momentum, learning rate, hidden layer untuk menghasilkan keakuratan yang tinggi. Temuan yang diperoleh berupa rancangan optimal untuk melakukan prediksi yaitu dengan menggunakan multilayet. Diperoleh tingkat akurasi mencapai 98.324% atau dengan tingkat eror yang relatif rendah yaitu 11,161%
Teknologi Blockchain berbasis Non Fungible Token sebagai Penghargaan Partisipasi Donor Darah Moleo, Alif Safa; Hasanuddin, Tasrif; Darwis, Herdianti; Harlinda, Harlinda
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28005

Abstract

Non-fungible tokens (NFTs) are a technological innovation that has been widely used to provide a form of digital reward. However, the application of NFTs in the social domain, especially in blood donation programs, has not been widely explored. This research aims to develop an NFT-based reward system using blockchain technology as an appreciation for blood donors. The system is designed and developed using the Ethereum test network due to its stability in decentralized applications. This research uses the research and development (R&D) method with the 4D model approach, which consists of the Define, Design, Develop, and Disseminate stages. In the Define stage, a needs analysis was conducted to determine the system specifications. The Design stage involves the design of a web-based system3 to support NFT management. In the Develop stage, the system was developed using the Ethereum testing network. The Disseminate stage includes system testing using the black box method to ensure that all key features, such as NFT claims and data transparency, function properly. The result of the research is an NFT-based blockchain application that allows blood donors to easily claim their NFTs as a form of digital recognition. The evaluation showed an acceptability score of 61.34%, indicating that this application is acceptable to the community and has the potential to increase blood donor motivation. The implementation of this system is expected to have a sustainable positive impact on increasing blood donor participation in the future.
Analysis of ensemble machine learning classification comparison on the skin cancer MNIST dataset Belluano, Poetri Lestari Lokapitasari; Rahma, Reyna Aprilia; Darwis, Herdianti; Rachman Manga, Abdul
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p235-242

Abstract

This study aims to analyze the performance of various ensemble machine learning methods, such as Adaboost, Bagging, and Stacking, in the context of skin cancer classification using the skin cancer MNIST dataset. We also evaluate the impact of handling dataset imbalance on the classification model’s performance by applying imbalanced data methods such as random under sampling (RUS), random over sampling (ROS), synthetic minority over-sampling technique (SMOTE), and synthetic minority over-sampling technique with edited nearest neighbor (SMOTEENN). The research findings indicate that Adaboost is effective in addressing data imbalance, while imbalanced data methods can significantly improve accuracy. However, the selection of imbalanced data methods should be carefully tailored to the dataset characteristics and clinical objectives. In conclusion, addressing data imbalance can enhance skin cancer classification accuracy, with Adaboost being an exception that shows a decrease in accuracy after applying imbalanced data methods.
Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning Purnawansyah, Purnawansyah; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Haviluddin, Haviluddin; Raja, Roesman Ridwan; Darwis, Herdianti; Nafalski, Andrew
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.333

Abstract

Network congestion arises from factors like bandwidth misallocation and increased node density leading to issues such as reduced packet delivery ratios and energy efficiency, increased packet loss and delay, and diminished Quality of Service and Quality of Experience. This study highlights the potential of deep learning and ensemble learning for network congestion analysis, which has been less explored compared to packet-loss based, delay-based, hybrid-based, and machine learning approaches, offering opportunities for advancement through parameter tuning, data labeling, architecture simulation, and activation function experiments, despite challenges posed by the scarcity of labeled data due to the high costs, time, computational resources, and human effort required for labeling. In this paper, we investigate network congestion prediction using deep learning and observe the results individually, as well as analyze ensemble learning outcomes using majority voting, from data that we recorded and clustered using K-Means. We leverage deep learning models including BPNN, CNN, LSTM, and hybrid LSTM-CNN architectures on 12 scenarios formed out of the combination of level datasets, normalization techniques, and number of recommended clusters and the results reveal that ensemble methods, particularly those integrating LSTM and CNN models (LSTM-CNN), consistently outperform individual deep learning models, demonstrating higher accuracy and stability across diverse datasets. Besides that, it is preferably recommended to use the QoS level dataset and the combinations of 3 clusters due to the most consistent evaluation results across different configurations and normalization strategies. The ensemble learning evaluation results show consistently high performance across various metrics, with accuracy, Matthews Correlation Coefficient, and Cohen's Kappa values nearing 100%, indicates excellent predictive capability and agreement. Hamming Loss remains minimal highlighting the low misclassification rates. Notably, this study advances predictive modeling in network management, offering strategies to enhance network efficiency and reliability amidst escalating traffic demands for more sustainable network operations.
Co-Authors Adela Regita Azzahra Adnan, Adam Aji Prasetya Wibawa Alfitriana Riska Annisa Febrianti Arfah, Jumrayanti Asfar, A. Muh. Fitrah Astuti, Wistiani Atmajaya, Dedy Atussaliha, Nur Almar' Azwan Triyadi Basri, Haerunnisa Basri, Nur Anisa Dian Fitrah Yusti Randana Harlinda, Harlinda Hasanuddin, Tasrif Haviluddin Haviluddin Lilis Nur Hayati lilis nurhayati Lokapitasari Belluano, Poetri Lestari Magfirah, Magfirah Mardiyyah Hasnawi Moleo, Alif Safa Munaf, Adryan Dwiprawira Nafalski, Andrew Nur wahidah Purnawansyah Purnawansyah Purnawansyah Rachman Manga, Abdul Rahma, Reyna Aprilia Raja, Roesman Ridwan Salim, Yulita Sastra, Ramdan Siska Anraeni Triyanna Widiyaningtyas Triyanna Widyaningtyas, Triyanna Umar, Fitriyani Veithzal Rivai Zainal Wanaspati, Nugraha Wati, Silmi Nur Zaskia Widyawati, Dewi