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All Journal TEKNIK INFORMATIKA PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal sistem informasi, Teknologi informasi dan komputer E-Dimas: Jurnal Pengabdian kepada Masyarakat Format : Jurnal Imiah Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah FIFO Jurnal Sisfokom (Sistem Informasi dan Komputer) Kilat JURIKOM (Jurnal Riset Komputer) IJISCS (International Journal Of Information System and Computer Science) JSAI (Journal Scientific and Applied Informatics) JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Pasopati : Pengabdian Masyarakat dan Inovasi Pengembangan Teknologi RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Dedikasi Nusantara: Jurnal Pengabdian Masyarakat Pendidikan Dasar Madani : Jurnal Pengabdian Kepada Masyarakat JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Journal Collabits IJISCS (International Journal of Information System and Computer Science)
Salamah, Umniy
Universitas Mercu Buana
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : JSAI (Journal Scientific and Applied Informatics)

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Perbandingan Metode Pembelajaran Mesin Berbasis Parametrik dan Non-Parametrik Untuk Klasifikasi Diabetic Retinopathy Imagery Salamah, Umniy
JSAI (Journal Scientific and Applied Informatics) Vol 4, No 2 (2021): Juni 2021
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v4i2.1668

Abstract

Untuk mendeteksi kerusakan retina dapat dilakukan bantuan algoritmapembelajaran mesin. Klasifikasi citra dengan menggunakan machine learningtechniques (MLTs) dapat membantu proses penentuan pasien penderitadiabetic retinopathy (DR). Teknik machine learning yang digunakan dapatdikelompokkan menjadi nonparametric (support vector machine) danparametric (logistic regression). Tahap penelitian termasuk persiapan,ekstraksi fitur, normalisasi, klasifikasi, evaluasi dilakukan terhadap datasetgambar digital fundus yang disediakan oleh EyePACS. Model klasifikasimenggunakan model nonparametric (support vector machine) dan parametric(logistic regression). Sebagai hasil, metode logistic regression mendapatkanhasil akurasi (accuracy) sebesar 74%, recall sebesar 74%, presisi (precision)sebesar 60% dan F1-score sebesar 63%. Selain itu, metode support vectormachine mendapatkan hasil akurasi (accuracy) sebesar 74%, recall sebesar74%, presisi (precision) sebesar 55% dan F1-score sebesar 63%
Perbandingan Metode Pembelajaran Mesin Berbasis Parametrik dan Non-Parametrik Untuk Klasifikasi Diabetic Retinopathy Imagery Umniy Salamah
JSAI (Journal Scientific and Applied Informatics) Vol. 4 No. 2 (2021): Juni 2021
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v4i2.1668

Abstract

Untuk mendeteksi kerusakan retina dapat dilakukan bantuan algoritmapembelajaran mesin. Klasifikasi citra dengan menggunakan machine learningtechniques (MLTs) dapat membantu proses penentuan pasien penderitadiabetic retinopathy (DR). Teknik machine learning yang digunakan dapatdikelompokkan menjadi nonparametric (support vector machine) danparametric (logistic regression). Tahap penelitian termasuk persiapan,ekstraksi fitur, normalisasi, klasifikasi, evaluasi dilakukan terhadap datasetgambar digital fundus yang disediakan oleh EyePACS. Model klasifikasimenggunakan model nonparametric (support vector machine) dan parametric(logistic regression). Sebagai hasil, metode logistic regression mendapatkanhasil akurasi (accuracy) sebesar 74%, recall sebesar 74%, presisi (precision)sebesar 60% dan F1-score sebesar 63%. Selain itu, metode support vectormachine mendapatkan hasil akurasi (accuracy) sebesar 74%, recall sebesar74%, presisi (precision) sebesar 55% dan F1-score sebesar 63%
Prediksi Rating Film Menggunakan Bayesian Regressor dan Gradient Boosting Regressor Umniy Salamah
JSAI (Journal Scientific and Applied Informatics) Vol 5 No 3 (2022): November 2022
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v5i3.3614

Abstract

Salah satu fitur yang cukup banyak dikembangkan untuk aplikasi adalah fitur penilaian pengguna. Informasi tentang peringkat pengguna ini dapat digunakan untuk  memberikan rekomendasi terbaik tentang hal menarik bagipengguna lainnya. Sebagai contoh, layanan untuk penjualan film, fitur ini dapat digunakan untuk memberikan rekomendasi yang sesuai dengan peringkat pengguna dan mendorong peningkatan penjualan. Adapun tahapan penelitian adalah Data Preprocessing, Feature Engineering, Modelling dan Evaluation. Penelitian ini menggunakan metode yaitu Bayesian Regressor dan Gradient Boosting Regressor untuk memprediksi movie rating. Penelitian inimenggunakan TMDB 5000 Movie Dataset yang terdiri dari kurang lebih 4800 data. Sebagai hasilnya, Gradient Boosting Regressor memiliki hasil yang lebih baik dibandingkan Bayesian Ridge Regressor. Gradient Boosting Regressor memiliki nilai R^2 score sebesar 0.843.
Automated Fruit Classification Menggunakan Model VGG16 dan MobileNetV2 Umniy Salamah; Anita Ratnasari; Sarwati Rahayu
JSAI (Journal Scientific and Applied Informatics) Vol 5 No 3 (2022): November 2022
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v5i3.3615

Abstract

Pengembangan robot atau mesin untuk membantu kegiatan pertanian memerlukan riset yang panjang. Teknologi tersebut harus dapat memiliki keahlian dalam melakukan berbagai macam aktivitas dan mampu mendeteksi objek yang menjadi sasaran pekerjaannya. Untuk memenuhi hal ini, riset untuk mendeteksi objek pertanian, misalnya buah, menjadi salah satu agenda riset yang perlu dilakukan dan dikembangkan. Tujuan penelitian ini adalah untuk mengetahui hasil perbandingan performa deep learning yaitu VGG16 dan MobileNetV2 untuk fruit classification. Penelitian ini menggunakan dataset dengan jumlah total 90.483 data dengan ukuran gambar 100x100 piksel dan jumlah kelas tanaman buah yang akan diklasifikasi adalah sebanyak 131 kelas. Pada proses testing menggunakan dataset yang ada, MobileNetV2 mendapatkan akurasi 98.4% dan ResNet50 mendapatkan akurasi 99,2%.
Analisis Perbandingan Metode Dempster Shafer Dan Certainty Factor Dalam Mendeteksi Penyakit Anemia Umniy Salamah; Richi Ramadhan
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.v6i2.5177

Abstract

Anemia is one of the nutritional disorders that are still often found in Indonesia. The Household Health Survey found that around 57% of Indonesian adolescent girls aged 10-14 years and 39.5% of women aged 15 years and over still suffer from anemia. From the high cases of anemia sufferers, researchers aim to help the community in preventing and treating patients by providing information related to the symptoms of anemia, so that researchers will create an expert system that can diagnose anemia. An expert system is a computer-based system that uses knowledge, facts, and reasoning techniques to solve problems that can usually only be solved by an expert in a particular field. The purpose of developing expert systems is actually not to replace the role of humans, but to transfer human knowledge into the form of systems, so that they can be used by many people and are not limited by time. In this study, researchers want to build an expert system that can detect anemia, where in this study the researcher will compare the Dempster-Shafer method and Certainty Factor, where the system is built using PHP programming language and MySQL database that can be accessed anytime and anywhere so as to make it easier for patients to diagnose anemia without the need to meet directly with experts.
Pengaruh Bilateral Filter Pada Algoritma Support Vector Machine Untuk Klasifikasi Produk Sayuran Umniy Salamah
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.v6i3.5899

Abstract

The agricultural industry is now applying artificial intelligence-based classification methods for the analysis of vegetable products. This study aims to classify vegetable products as part of the research of the classification of objects in charge that are inherently more complex than other subsets of object classification. In this research, the classification model will use the image preprocessing method on the support vector machine (SVM) algorithm. The dataset of this study amounted to 21,000 data with the division of training data (15,000 data), testing data (3,000 data) and validation data (3,000 data). In this study, experiments from the implementation of bilateral filter and support vector machine (SVM) methods obtained the highest accuracy of 70.59%. This experiment uses bilateral filters with parameters 15, 75, and 75. Other experiments obtained accuracy of 34.21% (histogram equalization) and 65.68% (colour space conversion).
Model Extreme Gradient Boosting Berbasis Term Frequency (TFXGBoost) Untuk Klasifikasi Laporan Pengaduan Masyarakat Vina Ayumi; Desi Ramayanti; Handrie Noprisson; Yuwan Jumaryadi; Umniy Salamah
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 1 (2023): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i1.6089

Abstract

Various algorithms and machine learning techniques are being applied to improve the efficiency and effectiveness of the process of automatically classifying complaint reports from the public in Indonesia. One machine learning algorithm that has recently gained benchmarks in the state of the art of various problems in machine learning is eXtreme Gradient Boosting (XGBoost). This study aims to develop an extreme gradient boosting model based on term frequency (TFXGBoost) to predict whether a text is classified as a complaint or not a complaint based on the data studied. Based on the experimental results, TFXGBoost achieved 92.79% accuracy with eta / learning rate hyperparameters of 0.5, gamma of 0, and max_depth of 3 and the computation time required to adjust the hyperparameters was 13870.012468 seconds.
Pengaruh Tuning Parameter dan Cross Validation Pada Klasifikasi Teks Komplain Bahasa Indonesia Menggunakan Algoritma Support Vector Machine Vina Ayumi; Desi Ramayanti; Handrie Noprisson; Anita Ratnasari; Umniy Salamah
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.v6i3.6117

Abstract

Text classification aims to group text data, for example, to find some information from a large social media text dataset so that it can be used by the data owner. Manual text classification is time-consuming and difficult, so some researchers try to research text classification automatically. This study attempts to classify Indonesian text datasets using the SVM algorithm. The research was conducted in two stages, namely the first experiment without cross validation parameters and parameter tuning, then the second experiment was carried out with cross validation parameters and parameter tuning. Experiments without cross validation parameters and parameter tuning for support vector machines (SVM) obtained 89.47% accuracy with precision and recall values of 0.90 and 0.89 respectively. The second experiment used cross validation with k-5 and k-10 and tuning parameters with C constant and gamma values. Cross validation results with k-10 obtained the best accuracy with a value of 96.48% with a computation time of 40.118 seconds. Next, kernel functions in tuning parameters namely sigmoid, linear and radial basis functions are analyzed and it is found that sigmoid kernel functions achieve the best accuracy and computational time.
Komparasi Hasil Algoritma Machine Learning Berbasis HSV Color Model Untuk Klasifikasi Citra Jenis Sayuran Umniy Salamah
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.v7i2.6392

Abstract

Currently, research on the classification of vegetables has made many advances. Machine learning has been proposed in recent years and has been created in image recognition, computer vision, and other fields. This study aims to classify vegetable products as part of the research of the classification of objects in charge that are inherently more complex than other subsets of object classification. This study will use the K-Nearest Neighbor (KNN) model to classify vegetable species, but with the addition of HSV color space model features. To see the performance of K-Nearest Neighbor (KNN) against other machine learning algorithms, a comparison will be made with support vector machine algorithms, logistic regression and naïve bayes. From the experimental results, the KNN algorithm got an accuracy of 80.67%, SVM got an accuracy of 72.23%, LR got an accuracy of 61.19%, NB got an accuracy of 48.77% and HSV-KNN got an accuracy of 84.33%.
Classification of Text Datasets of Public Complaints Against the Government on Social Media Using Logistic Regression Purba, Mariana; Dianing Asri, Sri; Ayumi, Vina; Salamah, Umniy; Iryani, Lemi
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 1 (2024): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i1.6447

Abstract

Di era teknologi saat ini, salah satu media sosial yang banyak digunakan dalam berinteraksi dan memberikan opini, pengaduan masyarakat, serta saran adalah Twitter. Dalam bidang pemerintahan, tweet yang mengandung opini atau pengaduan masyarakat terhadap suatu layanan atau program organisasi dapat digunakan sebagai umpan balik untuk memperbaiki atau meningkatkan kualitas layanan. Penelitian ini berfokus pada klasifikasi tweet untuk membedakan tweet yang tergolong pengaduan masyarakat atau non-pengaduan masyarakat dengan menerapkan algoritma pemelajaran mesin yaitu logistic regression (LR). Tahap dari penelitian ini antara lain crawling dan labeling dataset, pre-processing, pemodelan menggunakan classifier logistic regression, serta evaluasi kinerja classifier. Tahapan dalam penelitian ini seperti preprocessing, klasifikasi dan evaluasi dilakukan menggunakan bahasa pemrograman Python dengan bantuan scikit-learn library. Berdasarkan hasil eksperimen, model penelitian dengan menggunakan fitur ekstraksi CountVectorizer mencapai kinerja yang lebih baik daripada TfidfVectorizer. Eksperimen dengan menggunakan ekstraksi fitur TfidfVectorizer mencapai akurasi 92% (F1 score: 0.9181, precision: 0.9191 recall: 0.9181, kappa: 0.8363) sedangkan menggunakan akurasi CountVectorizer mencapai 94% (F1 score: 0.9355, precision: 0.9406, recall: 0.9356, kappa: 0.8715).
Co-Authors Ahmad Ali Mutezar Aldi Rahman Andi Nugroho, Andi Andi Purnomo Andrean, Rifky Anita Ratnasari Anjar Hero Wilarto Arey Nur Warisman Arrachman, Abdul Kholiq Athillah, Naufal Ayumi, Vina Bagus Priambodo Bagus Priambodo Bagus Priambodo Bagus Priambodo Chairulsyah, Diofavian Rafif Desi Ramayanti Dheo Alpian Nanda Krisna Dianing Asri, Sri Eka Maulana Fajriah, Riri Fajrin Nur Utami Fani Putri Oktavia Fauzi Nur Iman Gusty Age Rusandy Handrie Noprisson Ida Farida Ida Farida Inge Handriani, Inge Irene Ananda Iryani, Lemi Ivan Hardi Pratama Jumaryadi, Yuwan Kurnia Kito, Ramadani Lystha, Arie Maritza, Kaka Irsyad Noprisson, Handrie Nur Iman, Fauzi Panggabean, Claudio Nehemia Prasha, Achmad Ardani Purba, Mariana Putra, Erwin Dwika Richi Ramadhan Sambada, Arga Sarwati Rahayu Sarwati Rahayu Sarwati Rahayu Setiawan, Hadiguna Soemantri, Syauqi Adli Sulis Sandiwarno, Sulis Suratno, Igo Prayoga Syifa Fauziah Vera Yunita Vina Ayumi Vincentius Krisna Aditya Warisman, Arey Nur Wibowo, Aditya Dwi Zamzami, Muhammad Aryaka