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All Journal JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Jurnal Informatika dan Teknik Elektro Terapan E-Dimas: Jurnal Pengabdian kepada Masyarakat Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Computatio : Journal of Computer Science and Information Systems Jurnal CoreIT Jurnal Eksplora Informatika SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Simtek : Jurnal Sistem Informasi dan Teknik Komputer Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat ComTech: Computer, Mathematics and Engineering Applications Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan JISKa (Jurnal Informatika Sunan Kalijaga) INTI Nusa Mandiri Jurnal Mnemonic Jurnal Bakti Masyarakat Indonesia Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Informatika Progres Internet of Things and Artificial Intelligence Journal Jurnal Pengabdian Pada Masyarakat Edu Komputika Journal Jurnal Serina Abdimas Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Teny Handhayani
Universitas Tarumanagara
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Analysis and Prediction of Foodstuffs Prices in Tasikmalaya Using ELM and LSTM Winata, Andry; Lauro, Manatap Dolok; Handhayani, Teny
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3145

Abstract

Foodstuffs price analysis and prediction is one of the important research topics. This paper applies Long Short-Term Memory (LSTM) and Extreme Learning Machines (ELM) as models for forecasting the price of rice, chicken meat, chicken egg, shallot, garlic, and red chili in the Tasikmalaya traditional market. The dataset is a daily time series obtained from April 2017 - February 2023. LSTM models perform accurately to forecast 5 foodstuffs prices and obtain MAPE scores of no more than 3%. ELM works well to predict the price of rice, chicken meat, chicken egg, shallot, and garlic with MAPE scores are less than 1%. The price of rice, chicken egg, shallot, and red chili has an increasing trend. The correlation analysis finds that the price of chicken egg, shallot, and red chili has a positive correlation with each other.
Air Quality Index Classification for Imbalanced Data using Machine Learning Approach Jayadi, Bryan Valentino; Lauro, Manatap Dolok; Rusdi, Zyad; Handhayani, Teny
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3503

Abstract

Air pollution is one of the problems in society. Air pollutions affect human health and environment. In Indonesia, air quality index is measured by the level of particulate matter 10 (PM10), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and nitrogen dioxide (NO2). This research is conducted to evaluate the performance of machine learning algorithms, e.g., Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree, and AdaBoost, to classify air quality index based on the level of PM10, CO, SO2, O3, and NO2 with imbalanced samples. The air quality index is classified into Good, Moderate, and Unhealthy. The dataset is downloaded from Open Data Jakarta from 2010 -2021. The data containing 4383 samples consist of 1155 samples of Good, 3087 samples of Moderate, and 141 samples of Unhealthy. The experimental results show that Decision Tree outperforms other methods. Decision Tree produces accuracy, precision, recall, and F1-score of 99%, 98%, 99%, and 98%, respectively.
PENERAPAN LSTM DAN GRU UNTUK PREDIKSI HARGA CABAI MERAH DI KOTA JAWA TIMUR Lim, Maggie; Handhayani, Teny
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6467

Abstract

Fluktuasi harga cabai merah di Jawa Timur, yang dipengaruhi oleh berbagai faktor seperti musim tanam, cuaca, dan permintaan pasar, menjadi perhatian penting dalam menjaga stabilitas ekonomi. Dalam penelitian ini, digunakan dua algoritma Recurrent Neural Networks (RNN), yaitu Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU), untuk memprediksi harga cabai merah di Jawa Timur. Pengujian dilakukan dengan menggunakan dua skenario data latih, yaitu 70% dan 80%, dengan jumlah epoch tetap sebanyak 50. Hasil pengujian menunjukkan bahwa LSTM memberikan hasil yang lebih baik pada skenario 80% data latih, dengan nilai Mean Absolute Error (MAE) sebesar 1458,764, Root Mean Squared Error (RMSE) 2596,010, dan koefisien determinasi (R²) 0,978. Sementara itu, GRU menunjukkan sedikit keunggulan pada 70% data latih, dengan MAE 1742,027, RMSE 2820,462, dan R² 0,969. Secara keseluruhan, LSTM lebih optimal pada jumlah data latih yang lebih besar, sedangkan GRU lebih stabil pada data latih yang lebih kecil. Penelitian ini menyarankan pemilihan algoritma berdasarkan jumlah data latih yang tersedia untuk prediksi harga cabai merah yang lebih akurat.
Pemanfaatan Website untuk Otomasi Manajemen Salon di Bekasi Handhayani, Teny; Wasino, Wasino; Pragantha, Jeanny; Mahendra, Izam Susilo
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 15, No 3 (2024): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v15i3.18334

Abstract

Aplikasi berbasis web menjadi salah satu pendukung bisnis di era modern. Aplikasi berbasis web dapat meningkatkan layanan bisnis. Kegiatan pengabdian kepada masyarakat ini bekerjasama dengan salah satu salon kecantikan di kota Bekasi. Tim PKM terdiri atas dosen dan mahasiswa. Pokok kegiatan ini yaitu mengembangkan aplikasi berbasis web untuk mitra. Aplikasi didesain menyediakan beberapa fasilitas utama yaitu menampilkan informasi tentang layanan yang disediakan oleh salon, pemesanan layanan secara online, pencatatan transaksi layanan, dan membuat laporan. Pengembangan website melibatkan mitra sebagai pengguna. Mitra berpartisipasi memberikan informasi mengenai fasilitas aplikasi yang mereka butuhkan. Tim PKM bertindak sebagai pengembang aplikasi. Pengujian aplikasi dilakukan oleh mitra dan pelanggan yang dipilih secara acak. Berdasarkan penilaian dari penguji, aplikasi yang dikembangkan memenuhi kebutuhan mitra dan dapat meningkatkan layanan salon kepada pelanggan.
Prediksi Harga Emas di Indonesia Menggunakan Gated Recurrent Unit Handhayani, Teny; Tanudy, Clara; Hendryli, Janson
JURNAL FASILKOM Vol. 13 No. 3 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i3.6185

Abstract

Prediction system for the price of gold in Indonesia using a machine learning algorithm, namely the Gated Recurrent Unit (GRU), with influencing variables being the closing price of PT. Aneka Tambang's stock and the closing price of the US dollar exchange rate. The main objective of developing this system is to provide accurate and reliable information about the gold price trends for the next 7 days to the general public, investors, and other relevant parties. The dataset used consists of historical data for the closing prices of gold, the closing prices of PT. Aneka Tambang's stock, and the closing prices of the US dollar exchange rate, obtained from Yahoo Finance's website from January 2018 to October 2023. The dataset was pre-processed by extracting the dates from the three data sources used. In the results of training the GRU model for prediction, the best results were achieved with hyperparameters of 70% training data, 30% testing data, a timestep of 20, 50 epochs, and a batch size of 16, with an R-Squared value of 0.97, an MAE of 300.17, and an RMSE of 17.33. With the development of this system, it is expected to provide guidance for the general public, investors, and related parties in making timely decisions regarding gold purchases and to enhance their understanding of gold price movements in Indonesia.
PENGENALAN KUE TRADISIONAL INDONESIA MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK Lubis, M.Kom., Chairisni; Sumarlie , Devid; Handhayani, Teny
Computatio : Journal of Computer Science and Information Systems Vol. 6 No. 2 (2022): 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.v6i2.21098

Abstract

In Indonesia, a lot of cakes are included in the category of traditional snacks. Traditional snacks are a unique culture of the archipelago that must be preserved by Indonesians. Traditional cakes are snacks that people like because they are dense and filling. Traditional cakes have a variety of textures, shapes and colors are very diverse and some are similar to each other, so it is rather difficult to identify the cake. The problem faced by buyers is that they often do not know the name of a cake because of the many types of cakes sold in the market. Technological advances have also caused many local people to use social media to take photos of food, but to recognize these cakes, there are still many people who do not really understand traditional cakes compared to modern cakes. The above problem can be solved if a system is made to recognize the image/photo of the cake and the computer can be programmed and to classify the cake into a certain category of cake by utilizing the image of the cake using the Convolutional Neural Network (CNN) algorithm. The best test results are tests that include data augmentation during training, where VGG-16 has a higher accuracy than DenseNet121 which is 80% and DenseNet121 testing which uses k-fold cross validation with an accuracy of fold 1 which is 77% and a drastic increase up to fold 5. If without using data augmentation, the best result obtained is an accuracy of 83% achieved by DenseNet121 without transfer learning, learning rate 1e-5 and batch size 16.
PENINGKATAN AKTIVITAS PEMASARAN UMKM PEMPEK DAVIN MELALUI PENGEMBANGAN WEBSITE Lewenusa, Irvan; Teny Handhayani; Cecillia Chung
Jurnal Serina Abdimas Vol 3 No 2 (2025): Jurnal Serina Abdimas
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jsa.v3i2.34794

Abstract

Indonesian SMEs contribute 15.8% to the global production supply chain at the ASEAN level. Operating in the culinary sector of Palembang specialties, UMKM Pempek Davin has the potential to expand further. If relying on conventional marketing, the marketing reach is limited. This MSME is trying to pioneer the development of its marketing potential through digital media to face the increasingly tight business competition in the digital era (start-up). Therefore, this Community Service (PKM) aims to enhance marketing efforts through website creation. The website development method includes business needs analysis, system design, website development, as well as training and mentoring in website management and digital marketing strategies. This activity is carried out in several stages, starting from data collection on the current marketing conditions, website design and implementation, and concluding with an assessment of how effectively the website improves visibility and product sales. The result of this activity is the creation of a marketing website that allows UMKM Pempek Davin to promote and sell its products online. Additionally, the UMKM owners were trained to manage the content on the website and understand effective digital marketing strategies, enabling them to expand their marketing reach through search engines and enhance the credibility of UMKM Pempek Davin by having an official digital media presence. Next, the website maintenance process will be carried out in the form of knowledge transfer from the PKM implementation team to the partner team, including the handover of the website's source code, website administrator accounts, and website usage instructions to the staff designated by the partner team. ABSTRAK UMKM Indonesia memiliki kontribusi sebesar 15.8% terhadap rantai pasok produksi global di tingkat ASEAN. Bergerak di sektor kuliner khas Palembang, UMKM Pempek Davin memiliki potensi untuk berkembang lebih luas. Jika mengandalkan pemasaran konvensional maka jangkauan pemasaran terbatas. UMKM ini mencoba untuk merintis mengembangkan potensi pemasarannya melalui media digital untuk menghadapi persaingan bisnis yang semakin ketat di era digital (start-up). Oleh karena itu, Pengabdian Kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan upaya pemasaran melalui pembuatan website. Metode pengembangan website mencangkup analisis kebutuhan bisnis, perancangan sistem, pengembangan website, serta pelatihan dan pendampingan dalam pengelolaan website dan strategi pemasaran digital. Kegiatan ini dilakukan dalam beberapa tahap, dimulai dari pengumpulan data tentang kondisi pemasaran saat ini, perancangan dan implementasi website, dan selesai dengan menilai seberapa efektif website meningkatkan visibilitas dan penjualan produk. Hasil dari kegiatan ini adalah pembuatan website pemasaran yang memungkinkan UMKM Pempek Davin untuk mempromosikan dan menjual produknya secara online. Selain itu, pemilik UMKM dilatih untuk mengelola konten di website tersebut dan memahami strategi pemasaran digital yang efektif sehingga dapat memperluas jangkauan pemasaran melalui mesin pencari dan meningkatkan kredibilitas UMKM Pempek Davin dengan memiliki media digital resmi. Selanjutnya, untuk proses pemeliharaan website akan dilakukan dalam bentuk knowledge transfer dari tim pelaksana PKM ke tim mitra meliputi penyerahan source code website, akun administrator website, serta petunjuk penggunaan website kepada staf yang ditunjuk oleh tim mitra.
PEMBUATAN APLIKASI BERBASIS WEBSITE UNTUK RESTORAN RR DI JAKARTA Handhayani, Teny; Daffa Hilmi Aji; Novario Jaya Perdana; Wasino; Jeanny Pragantha; Jason; Kelvin Wijaya
Jurnal Serina Abdimas Vol 3 No 2 (2025): Jurnal Serina Abdimas
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jsa.v3i2.34799

Abstract

A web-based application can be utilized to support small and medium-sized enterprises. A web framework or web application framework is software designed to support the development of web applications including web services, web resources, and web APIs. Community Service Activities are a forum for academics to implement research results to solve problems in society. This activity collaborates with the RR restaurant located in Jakarta. RR provides dine-in services by offering various types of food and beverage menus. This project was done from August 2024 to February 2025. It implements various methods, i.e., interviews, discussions, and mini workshops. The main problem is that restaurant management and services are still carried out manually and have not been implemented in information technology. Some of the problems are that the service process becomes more complicated when the restaurant is busy, and customer satisfaction decreases due to suboptimal service, for example, errors in delivering customer orders. Manual transaction recording becomes another problem in creating reports. The community service offers a solution to create website-based applications for the RR restaurant. The website created provides the main facilities, namely booking services, transaction recording, and data management. The web-based application created by the community service team is used to automate transaction, data recording and booking services. ABSTRAK Aplikasi berbasis website banyak dimanfaatkan untuk mendukung kegiatan bisnis Usaha Mikro Kecil dan Menengah (UMKM). Sebuah web framework atau web application framework merupakan perangkat lunak yang dirancang untuk mendukung pengembangan aplikasi web termasuk layanan web, sumber daya web, dan API web. Kegiatan Pengabdian Kepada Masyarakat (PKM) menjadi wadah bagi civitas akademika untuk menerapkan hasil penelitian guna membantu menyelesaikan permasalahan di masyarakat. Kegiatan PKM ini bekerjasama dengan mitra yaitu restoran RR yang terletak di Jakarta. RR menyediakan layanan makan ditempat dengan menawarkan berbagai jenis menu makanan dan minuman. Kegiatan ini dilaksanakan pada bulan Agustus 2024 sampai Februari 2025. Kegiatan PKM dilaksanakan menggunakan metode wawancara, diskusi, dan lokakarya mini. Permasalahan yang dihadapi mitra yaitu manajemen dan pelayanan restoran masih dilakukan secara manual dan belum menerapkan teknologi informasi. Beberapa kendala yang dihadapi oleh mitra yaitu proses pelayanan menjadi lebih rumit ketika restoran ramai dan kepuasan pelanggan menjadi berkurang karena layanan yang kurang optimal misalnya terjadi kesalahan menyampaikan pesanan pelanggan. Pencatatan transaksi yang masih manual juga menjadi kendala bagi karyawan untuk membuat laporan. Tim PKM menawarkan solusi untuk membuat aplikasi berbasis website untuk mitra. Website yang dibuat oleh tim PKM menyediakan fasilitas utama yaitu layanan pemesanan, pencatatan transaksi, dan manajemen data. Aplikasi berbasis web ini dimanfaatkan untuk otomasi penyimpanan data transaksi, dan memudahkan pelayanan pemesanan.
A New Approach for Dynamic Analysis of Indonesian Food Prices using the PC Algorithm and Vector Autoregression Handhayani, Teny; Permana, Yudistira; Farouqi, Akmal; Firdausyan, Naufal; Sonata, Raffy; Yusuf Rumlawang Arpipi, Marcel; Lewenusa, Irvan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6601

Abstract

Food prices are important global issue and their relationship with fuel prices has become a main concern in society. An increase in the subsidized fuel price on 3 September 2022 has allegedly caused a rise in food (grocery) prices. This paper conducts an empirical study to analyze the relationships between food prices in Indonesia: rice, chicken, beef, egg, red chili, cayenne, shallot, garlic, cooking oil, and sugar. The study uses time series data of food prices from 1 January 2018 to 31 December 2023, which consists of food prices from 87 traditional markets in Indonesia. The commodity prices are obtained from online public data provided by Bank Indonesia. It divides the analysis (pre- and post-3 September 2022) to see how the relationship between food prices changes due to the increase in the subsidized fuel price. It performs the Peter Clark (PC) algorithm to generate causal graphs from real datasets where the true graphs are unknown, complements the analysis by performing Vector Autoregression (VAR) to investigate the dynamic relationship between food prices, especially how the subsidized fuel price increase changes its dynamic relationship. The causal graphs from pre- and post-increasing fuel prices show the changes in the role of variable relationships, e.g., sugar and beef. The VAR results also show an interesting change in the IRF pattern. The results from both the PC algorithm and VAR show that there is a structural change in the relationship between food prices and that there is a different effect of price shock due to the subsidized fuel price increase. It might have been an indication of a change in the consumption pattern in society as a response to a food price increase. This must be a huge task to do in maintaining food prices when there is an adjustment in the subsidized fuel prices.
A Comparison of Machine Learning and Deep Learning Methods for Temperatures Predictions on Java Island Handhayani, Teny; Hendryli, Janson; Pragantha, Jeanny; Wasino; Darius Andana Haris; Castello Purba, Andrew
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.23812

Abstract

Climate change is a global long-term change in temperatures and weather. Climate change is a worldwide issue that requires proper handling to reduce the negative impact on humans and the environment. Analyzing historical data is beneficial for studying climate change. Machine learning and deep learning methods are useful tools for data analysis. The goal of this paper is to find the best model for forecasting temperatures, a case study in Java Island. Java Island is the most densely island and the central economy and business in Indonesia. Climate change research in Java Island is important for sustainability. It runs several algorithms i.e., Gradient Boosting, AdaBoost, XGBoost, CatBoost, Light GBM, Random Forest, Support Vector Regression, Extreme Learning Machine, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The experiment uses a historical daily time series of temperatures from 1 January 1990 to 31 December 2024. In general, the experimental results show that Gradient Boosting produces the highest average coefficient of determination R2 scores of 0.34 and the lowest Mean Absolute Error scores of 0.69. Long Short-Term Memory and Gated Recurrent Units are the deep learning models that also work well for forecasting. According to the experimental results, in some cases, machine learning models outperform deep learning models and vice versa.
Co-Authors Adela Calista Adela Tania Adithya Putra, Farhan Agus Budi Dharmawan Andre Andre Andre Andre, Andre Andrian, Gion Andry Winata Angelica Christina Arya Bintang Saputra Arya Dwi Saputra Brando Dharma Saputra Castello Purba, Andrew Cecillia Chung Chairisni Lubis Cherissa Aeryn Djaya Christina, Angelica Daffa Hilmi Aji Dara Kharisma Limparan Darius Andana Haris David Jansen Dayanti, Afina Putri Desi Arisandi Desi Arisandi Desi Arisandi Djoenaedi, Owen Duncan Ariel Dwi Saputra, Arya Dyah Erny Herwindiati Dyah Erny Herwindiati Ericko, Teddy Eugene Supardi , Nicholas Faradila Herfiyana Farhan Afrial Farouqi, Akmal Fawaz Firdausyan, Naufal Gabriella Adeline Halim Georgia Sugisandhea Hendryli, Janson Herfiyana, Faradila Huang, Jervis Irvan Lewenusa Irvan Lewenusa Irvan Lewenusa, Irvan Janson Hendryli Janson Hendryli Jason Jaya, Jefri Jayadi, Bryan Valentino Jeanny Pragantha Jeanny Pragantha Jeanny Pragantha Jeremia Pinnywan Immanuel Jochsen, Erico Jong, Fenny Jordi Pradipta Kusuma Jourdan Stanley Julius Juan Karnadi, Benny Kelvin Wijaya Kelvin Wijaya Kusuma, Jordi Pradipta Lely Hiryanto Lim, Maggie Lubis, M.Kom., Chairisni Mahendra, Izam Susilo Mahendra, Izam Susilo Manatap Dolok Lauro Manatap Dolok Lauro, Manatap Dolok Manatap Sitorus Marchel Yusuf Rumlawang Arpipi Mathew Judianto Matthew Oni Matthew Russel Paul Mohammad Faraditya Eka Putra Monica Ong Muhammad Isnaini Syaifudin Nicko Kurniawan Novario Jaya Perdana Oni, Matthew Owen Maytrio Phratama Paulus Samotana Zalukhu Permana, Yudistira Peter James Tedja Phratama, Owen Maytrio Purba, Andrew Castello Sandy Permadi Sormin Sitorus Dolok Lauro , Manatap Sonata, Raffy Sopany, Mikael Reichi Sumarlie , Devid Sumarlie, Aurellia Clearesta Tanudy, Clara Tasya Syamsudin Tommy Wijaya Putra Tony Tony Veri Wasino Wasino Wasino Wasino Wasino Wasino Wasino, Wasino William William Winata, Andry Yusuf Rumlawang Arpipi, Marcel Zyad Rusdi