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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Computatio : Journal of Computer Science and Information Systems Jurnal Komunikasi Jurnal Pensil : Pendidikan Teknik Sipil Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis Infotech: Journal of Technology Information Humantech : Jurnal Ilmiah Multidisiplin Indonesia International Journal of Application on Economics and Business
Dyah Erny Herwindiati
Faculty of Information Technolgy Tarumanagara University
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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PERAMALAN DAN PENENTUAN WAKTU PEMESANAN KEMBALI YANG OPTIMAL DENGAN METODE EXPONENTIAL SMOOTHING DAN METODE ECONOMIC ORDER QUANTITY (STUDI KASUS SUMBER MAKMUR) Sukamdani, Ryan Rici; Herwindiati, Dyah Erny; Sutrisno, Tri
Jurnal Ilmu Komputer dan Sistem Informasi Vol 8, No 2 (2020): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v8i2.11538

Abstract

Application for forcasting the number of items the next day, determining optimal revervation time and determine the optimal number of or reorder items by using the method Single Exponential Smoothing, Double Exponential Smoothing, Economic Order Quantity(EOQ), and Reorder Quantity(ROP). This method was created for Store Sumber Makmur. Store Sumber Makmur is a shop that sells motorcycle tools, equipment, parts, accessories, lubricating oil, maintaince, and motorcycle repair services. There are  programming languages used to make this application, namely PHP.  php for the user interface and python for the calculation. Testing was done by User Acceptence Testing (UAT) and testing of data. UAT testing for check buttons and features
Implementasi Algoritma Convolutional Neural Networks Untuk Klasifikasi Jenis Cat Tembok Menggunakan Arsitektur MobileNet Carlos, Daniel; Herwindiati, Dyah Erny; Lubis, Chairisni
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5322

Abstract

The development of image recognition technology has made significant advancements, particularly with the emergence of Convolutional Neural Networks (CNN) algorithms. One of the CNN architectures that is efficient and effective for mobile devices is MobileNet. This study aims to implement the CNN algorithm using the MobileNet architecture for classifying types of wall paint. The main problem addressed is the accurate identification of wall paint types based on images, requiring a model that performs well even on devices with limited resources. MobileNet was chosen as the solution due to its ability to reduce computational complexity without sacrificing performance. The methodology used in this research involves two approaches: classification with feature extraction using GLCM and histogram, and classification without feature extraction directly using MobileNet. The training and testing process was conducted using the early stopping technique to prevent overfitting, with the model trained for 50 epochs. The final results show that classification without feature extraction using MobileNet yields excellent results. The model achieved a training accuracy of 89.68% and a testing accuracy of 88.86%, with low loss values (0.0111 for training and 0.0117 for testing). These results indicate that MobileNet is effective in recognizing and classifying types of wall paint and can operate efficiently on devices with limited resources. Therefore, this research demonstrates that using the MobileNet architecture for classifying wall paint types is an effective and efficient solution, opening opportunities for similar applications on various mobile devices in the future.
KLASIFIKASI BUAH BUSUK DAN BUAH MATANG BERDASARKAN DATA IMAGE MENGGUNAKAN MAHALANOBIS DISTANCE Handoyo, Rico; Herwindiati, Dyah Erny; Sutrisno, Tri
Humantech : Jurnal Ilmiah Multidisiplin Indonesia Vol. 2 No. 4 (2023): Humantech : Jurnal Ilmiah Multidisiplin Indonesia 
Publisher : Program Studi Akuntansi IKOPIN

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

Abstract

Buah merupakan komoditas penting dalam sektor pertanian dan sering digunakan sebagai bahan olahan di Indonesia. Selain sebagai komoditas, buah-buahan juga merupakan makanan pokok yang banyak diminati. Namun jika yang dikonsumsi masyarakat adalah buah yang sudah tidak segar lagi, maka akan menimbulkan penyakit bagi yang mengkonsuminya. Oleh karena itu, perlu dilakukan penelitian untuk mengklasifikasikan buah-buahan tersebut apakah busuk atau buah masak. Pada penelitian ini dataset citra apel, pisang, dan jeruk digunakan untuk mengekstraksi nilai karakteristik berdasarkan rata-rata warna RGB dan berdasarkan tekstur menggunakan haar wavelet. Setelah karakteristik diperoleh jarak akan dihitung menggunaan jarak mahalanobis dengan menghitung nilai rata-rata dan matriks kovarians. Penelitian ini menggunakan data training untuk mengambil nilai karakteristiknya dan data testing untuk mengklasifikasikannya dengan jarak mahalanobis berdasarkan berdasarkan nilai karakteristik pada data training. Hasil pengujian pada penelitian ini menunjukkan bahwa akurasi keseluruhan data citra apel, pisang, dan jeruk yang diuji menghasilkan akurasi sebesar 75,55 persen.
PENGENALAN BENTUK WAJAH DENGAN METODE CONVOLUTIONAL NEURAL NETWORK UNTUK PEMILIHAN MODEL KACAMATA SECARA ONLINE Budianto, Willson; Herwindiati, Dyah Erny; Hendryli, Janson
Infotech: Journal of Technology Information Vol 9, No 2 (2023): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v9i2.176

Abstract

Glasses were originally only a visual aid for someone who had a visual impairment, but over time glasses have developed into a fashion necessity. Glasses can generally be tried directly in optics, but due to the pandemic, there are restrictions on interaction so that glasses cannot be tried directly. This research discusses face shape recognition using the Viola Jones method and the Convolutional Neural Network method which is useful for providing recommendations for selecting glasses models via online. The input data is an external data from the Kaggle site which has five face shapes namely heart, rectangle, oval, round, and square. The training process is carried out to train the machine to recognize the user's face shape according to its class. The testing process provides accuracy results of 84.38% and macro average values for precision of 85%, recall of 85% and F1-Score of 84%. This system is expected to help users of glasses to choose a model of glasses that suits their face shape online.
Peramalan Pertumbuhan Jumlah Outlet Menggunakan Metode Gated Recurrent Unit (Studi Kasus: PT XYZ) Suluh, David; Herwindiati, Dyah Erny; Hendryli, Janson
Computatio : Journal of Computer Science and Information Systems Vol. 8 No. 1 (2024): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v8i1.21234

Abstract

Sebagai perusahaan telekomunikasi, PT XYZ mengguanakan outlet seluler sebagai mitra untuk melakukan pendistribusian komoditas mereka. Dalam memperluas jaringan outlet seluler merka, PT XYZ tentu perlu memikirkan strategi bisnis yang tepat agar pertumbuhan jumlah outlet dapat menjadi lebih maksimal.Peramalan dapat digunakan sebagai acuan dalam strategi bisnis dan meningkatkan efektivitas rencana penyebaran outlet. Penilitian ini membahas peramalan pertumbuhan jumlah outlet menggunakan metode Gated Recurrent Unit yang berfungsi untuk melakukan peramalan atau prediksi jumlah outlet yang dapat diraih oleh PT XYZ. Data yang digunakan merupakan data outlet yang ada di PT XYZ dimana data ini akan dikelempokkan berdasarkan minggu ketika outlet bergabung. Proses pelatihan data menggunakan 80% dari total dataset dan pengujian menggunakan 20% dari total dataset. Pada proses pengujian, model mendapatkan hasil evaluasi MAE sebesar 0.1230 ,RMSE sebersar 0.2103 dan MSE sebesar 0.0442.
Klasifikasi Kualitas Air Menggunakan K-Nearest Neighbors, Naïve Bayes, Dan Logistic Regression Denny, Mandy Sandra; Herwindiati, Dyah Erny
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 4 (2024): Oktober 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i4.1649

Abstract

Water is a natural resource that is very important for the life of living creatures on earth, but water is very easily contaminated with bacteria and dangerous substances. Therefore, it is important to pay attention to the quality of water on earth. To classify water quality as safe or unsafe, there are many methods that can be used. To choose the most suitable method, four methods were used, namely K-Nearest Neighbors (KNN), Naïve Bayes, and Logistic Regression. In this research, the dataset used is Water Quality from the Kaggle website which contains 7999 samples with 20 features and 1 target class. The aim of this research is to compare methods to obtain the highest accuracy values, accuracy results obtained from implementing algorithms in machine learning. The results obtained from the KNN, Naïve Bayes, and Logistic Regression methods were 89.62%, 78.69%, and 89.81% respectively. The highest accuracy result is Logistic Regression, so this method is the best method for classifying water quality data.
Prediksi Hujan Menggunakan Metode Artificial Neural Network, K-Nearest Neighbors, dan Naïve Bayes Martha, Regina; Herwindiati, Dyah Erny
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 4 (2024): Oktober 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i4.1650

Abstract

Rain is a natural phenomenon that has a significant impact on human life and ecosystems around the world. The ability to predict the weather, including predicting the next day's rain, has become an important aspect of our daily lives. Accurate rain predictions have broad implications, from planning outdoor activities to natural resource management, as well as controlling natural disasters. This research presents the results of an analysis of whether it will rain or not tomorrow based on 22 features, including location, temperature, wind speed, wind direction, humidity, and also the number of clouds covering the sky. In an effort to improve the accuracy of rain predictions, various methods have been developed, including Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Naive Bayes. After training and testing the models from these three methods, an evaluation was carried out using a confusion matrix and classification report to measure prediction performance. The experimental results show that ANN, KNN and Naive Bayes obtain accuracy scores of 85%, 84%, and 79%, respectively. So, it can be concluded that ANN is the best method for predicting tomorrow's rain.
Perbandingan Algoritma K-NN, SVM, dan Decision Tree dalam Klasifikasi Kelenjar Tiroid Angel, Angel; Herwindiati, Dyah Erny
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 4 (2024): Oktober 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i4.1651

Abstract

Thyroid disorders are a disease that is dif icult and often misdiagnosed. This is what causes many people to find out too late that they have this thyroid disorder. There are two types of thyroid disorders, namely hyperthyroidism and hypothyroidism. Machine Learning can be utilized to classify these disorders using data mining techniques. Classification is often used to predict many diseases, one of which is thyroid. The aim of this research was to determine the classification of the patient's thyroid. The data used is patient data sourced from Kaggle with 31 features (x) and 3 classes (y), namely 'Negative', 'Hypothyroid' and 'Hyperthyroid'. The data in this study was modeled using the Support Vector Machine (SVM) method with Radial Basis Function (RBF), K-Nearest Neighbor (KNN) and Decision Tree kernels. The results obtained are the percentage accuracy of each algorithm which is 97%, 92% and 91% respectively. From these results it can be concluded that the Support Vector Machine (SVM) algorithm is most suitable to be implemented with this dataset.
Pemetaan Kecamatan di Wilayah Bogor Berdasarkan Tipe Lahan dengan Metode Gradient Boosting Susilo, Venezia Valen; Herwindiati, Dyah Erny; Hendryli, Janson
Computatio : Journal of Computer Science and Information Systems Vol. 8 No. 2 (2024): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v8i2.15829

Abstract

Kabupaten Kota Bogor merupakan tempat Gunung Salak, sumber mata air Jakarta, berada sehingga untuk air sampai di Jakarta, air harus melalui Bogor terlebih dahulu. Hal ini mengakibatkan perubahan terhadap lahan di Bogor akan berpengaruh pada proses aliran air dari Gunung Salak ke Jakarta. Oleh karena itu, dibutuhkan suatu sistem yang dapat digunakan untuk memantau perubahan fungsi lahan di Bogor. Sistem ini, diharapkan dapat memberi informasi tentang alih fungsi lahan secara periodik yang terjadi di daerah Bogor dan diharapkan dapat membantu pihak-pihak yang terkait dalam penanganan dampak-dampak yang terjadi akibat alih fungsi lahan. Data yang diperlukan adalah citra Landsat 8 band 2, 3, 4, 5, 6, dan 7 yang telah melalui proses pra-pemrosesan untuk kemudian diklasifikasikan dengan menggunakan model yang dibangun dengan metode Gradient Boosting Regression untuk klasifikasi. Model dibangun dengan nilai learning rate 0.1 dan banyak pohon 50. Akurasi yang didapat dari model ini adalah 99.3349% untuk data latih, 99.1658% untuk data validasi, dan membutuhkan waktu 13.91376 detik.
Prediksi Jumlah Penduduk Tingkat Kecamatan di Wilayah Bogor Menggunakan Metode Long Short Term Memory Djoenaedi, Owen; Herwindiati, Dyah Erny; Handhayani, Teny
Computatio : Journal of Computer Science and Information Systems Vol. 8 No. 2 (2024): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v8i2.16219

Abstract

Population growth is addition or reduction of the population which is influenced by several factors. In Indonesia, this is something that pays great attention and is monitored by the government, especially on Java Island. Worries of population increase is one of the reasons for this monitoring which can cause problems with the support power and capacity power of the environment. The purpose of this design is to predict the population and calculate population growth rate at sub-district level in the Bogor area for 2021 and 2022 using population data at different annual intervals in each areas. Prediction is done using Long Short Term Memory. The configuration parameters of the model used for training and testing is different for each areas which obtained from the results of the parameter experiment which was repeated 5 times for each configuration to obtain the best Mean Absolute Percentage Error (MAPE) average. All models for LSTM method gain an average MAPE below 10% in each areas so that the models for prediction were stated to be very good.
Co-Authors Andri Muliawan Angel Angel Budianto, Willson Carlos, Daniel Chairisni Lubis Denny, Mandy Sandra Djoenaedi, Owen Ferdinand, Kelvin Gozali, Carisha Puspa Halim, Henry Handoyo, Rico Hendryli, Janson Karendef Karendef Lely Hiryanto Martha, Regina Muhammad Idrus Taba Nagaputra, Hongi Novario Jaya Perdana Otto Randa Payangan Pamungkas, Ary Satria Sani M. Isa Sari, Endah Murtiana Soelaiman, Lydiawati Stephen Christian, Stephen Sukamdani, Ryan Rici Suluh, David Susilo, Venezia Valen Teny Handhayani Tony Tony TRI SUTRISNO Wenda Kartika, Wenda Yasmin, Yamani