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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 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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Implementasi Mobile Web Pemesanan Dekorasi Wedding di Keiffa Decoration Adela Calista; Wasino; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 1 (2025): 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.v13i1.32895

Abstract

Di era digital, perkembangan teknologi informasi telah membawa dampak yang signifikan terhadap berbagai sektor jasa, termasuk dalam layanan dekorasi pernikahan. Keiffa Decoration, sebagai penyedia jasa dekorasi wedding, memanfaatkan platform mobile web untuk menyederhanakan proses pemesanan, meningkatkan efisiensi operasional, serta memperluas pangsa pasar. Penelitian ini berfokus pada desain dan implementasi sistem pemesanan online berbasis mobile web yang menyediakan fitur-fitur seperti galeri portfolio, pilihan paket dekorasi, penambahan dekorasi sesuai permintaan, serta fitur review pelanggan. Pengembangan sistem ini dilakukan dengan menggunakan HTML, CSS, dan JavaScript untuk antarmuka pengguna, sedangkan backend dibangun menggunakan framework Laravel dengan MySQL sebagai basis data. Metode pengembangan perangkat lunak yang digunakan adalah Waterfall, dimulai dari analisis kebutuhan, perancangan sistem, implementasi, hingga tahap pengujian. Hasil pengujian membuktikan bahwa sistem ini berhasil memenuhi kebutuhan operasional dan pelanggan, memudahkan proses pemesanan, mengurangi potensi kesalahan data, serta meningkatkan interaksi dan komunikasi antara bisnis dan pelanggan.
ANALISIS PERANCANGAN SISTEM INFORMASI PENGADUAN MAHASISWA DI FTI UNTAR MENGGUNAKAN PENDEKATAN UML Owen Maytrio Phratama; Teny Handhayani; Novario Jaya Perdana
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 1 (2025): 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.v13i1.32896

Abstract

The process of delivering student aspirations and complaints is one of the important aspects in enhancing the quality of education in higher education. One of them is the Faculty of Information Technology, Tarumanagara University, which is committed to providing high-quality academic and non-academic services. The current process of submitting aspirations and complaints is done manually because there is no centralized platform to collect complaint data, causing the handling of complaints to be hampered and less efficient. This research aims to analyze the design of a web-based student complaint information system in the FTI Untar environment to facilitate students in conveying aspirations and complaints. From the faculty side, this system also makes it easier to collect data on aspirations and complaints so they can respond to complaints more efficiently so as to improve the quality of education at FTI Untar. Data was gathered through a review of literature and interviews with FTI Untar stakeholders to collect and analyze user needs. System design analysis was conducted using UML (Unified Modeling Language) to visualize, design, and document various aspects of the system by describing use case diagrams, activity diagrams, class diagrams, and sequence diagrams, which help map workflows, main functions, and relationships between system components comprehensively.
DIAGRAM UNIFIED MODELLING UNTUK PERANCANGAN SISTEM PESAN JASA JAHIT Monica Ong; Wasino; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 1 (2025): 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.v13i1.32900

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem informasi berbasis daring untuk layanan pesan jahit di Toko Jahit Ahmad yang berlokasi di Kalideres, Jakarta Barat. Sistem ini dirancang menggunakan Unified Modeling Language (UML) untuk mengotomatisasi proses pencatatan pesanan, pengelolaan data pelanggan, dan pelacakan status pesanan. Metode yang digunakan dalam penelitian ini mencakup observasi langsung, wawancara, dan dokumentasi, yang memberikan gambaran lengkap mengenai proses bisnis jasa jahit tradisional yang saat ini diterapkan. Hasil dari implementasi sistem ini menunjukkan peningkatan efisiensi operasional, pengurangan kesalahan dalam pencatatan manual, serta kemudahan akses bagi pelanggan untuk memesan layanan secara daring. Dengan adanya sistem ini, diharapkan Toko Jahit Ahmad dapat memberikan layanan yang lebih cepat dan terstruktur, mendukung pengambilan keputusan strategis berbasis data yang terorganisir. Penelitian ini juga menyoroti potensi tantangan dalam pemeliharaan sistem dan adaptasi terhadap peningkatan jumlah pengguna.
Climate Change Sentiment Analysis using LSTM Marchel Yusuf Rumlawang Arpipi; Teny Handhayani; Janson Hendryli
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i1.1719

Abstract

This research aims to observe the sentiment of Indonesian people towards climate change using the Long Short-Term Memory (LSTM) methods. The data samples used in this study are primary data that have been collecting by using the Twitter Application Programming Interface (API) that provides by a platform known as RapidAPI. This data sample is text data with 2425 total samples obtained during the time period from 01 January 2020 to 25 August 2024. The sentiment is classified as positive, negative, and neutral. The performance of the LSTM model is evaluate using accuracy, precision, recall, F1-score, and confusion matrix and then compare with other models such as Ensemble Model, Naive Bayes, and Linear SVC. By conducting Exploratory Data Analysis (EDA), it is reveals that the distribution of public sentiment towards climate change in Indonesia from the collected data is mostly positive. However, there are not many individuals that are still ignorant and skeptical about the issue, resulting in a negative sentiment that can be fatal to the environment and its surroundings. When comparing the Ensemble Model, Naive Bayes, and Linear SVC, the LSTM model successfully identifies the perception patterns between sentences according to their sentiments. LSTM obtains an accuracy of 60% and outperforms Ensemble Model, Naive Bayes, and Linear SVC. This research also highlights the technical challenges in processing and analyzing dynamic and diverse data so that the results obtained are better, especially in terms of data quality before further processing.
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.
Clustering Data Meteorologi di Pulau Kalimantan Menggunakan Algoritma K-Means Kusuma, Jordi Pradipta; Lewenusa, Irvan; Handhayani, Teny
Eksplora Informatika Vol 14 No 2 (2025): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v14i2.1131

Abstract

Kalimantan merupakan salah satu pulau yang ada di wilayah Indonesia. Clustering data meteorologi pulau Kalimantan bertujuan untuk mengelompokkan kota-kota di wilayah tersebut guna mempelajari pertanda perubahan iklim. Artikel ini menggunakan data meteorologi time series harian dari 17 kota periode 1 Januari 2012 sampai 31 Juli 2023. Data dikumpulkan dari dari 17 kota yang tersebar di Pulau Kalimantan meliputi variabel temperatur minimum, temperatur maksimum, temperatur rata-rata, dan kecepatan angin rata-rata. Clustering dilakukan menggunakan metode K-Means dan K-Medoid. Metode Silhouette dan Davied Bouldin Index digunakan untuk memilih jumlah cluster optimal. Berdasarkan hasil evaluasi, metode K-Means mengungguli kinerja metode K-Medoid. Hasil eksperimen dengan menggunakan algoritma K-Means memperoleh jumlah cluster terbaik yaitu dua cluster dengan nilai Silhouette dan Davies Bouldin Index masing-masing sebesar 0.139 dan 1.923. Hasil clustering menggunakan metode K-Means memperoleh hasil kota Pontianak, Palangkaraya, Sambas, Ketapang, Sintang, Kapuas Hulu, Melawi, Kuburaya, Kotawaringin Barat, Kotawaringin Timur, Barito Selatan, dan Berau berada di Cluster 1. Tarakan, Balikpapan, Banjarmasin, Samarinda, dan Nunukan berada di Cluster 2. Trend tahunan variabel temperatur minimum di kota-kota cluster 1 mengalami kenaikan. Secara umum, tren tahunan menunjukkan bahwa kecepatan angin rata-rata dari tahun 2012 – 2023 mengalami penurunan. Kenaikan temperatur dan penurunan kecepatan angin menjadi tanda adanya perubahan iklim.
An Introduction to the Process of Making the Indonesian Handmade Batik Lasem Handhayani, Teny
ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2023): ABDIMAS UMTAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Muhammadiyah Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35568/abdimas.v6i2.3142

Abstract

Batik is an Indonesian culture that is appointed as an intangible culture by UNESCO in 2009. Batik in Indonesia is categorized as handmade and stamped according to the production process. Indonesian batik has different patterns and motifs depending on their origin. Batik Lasem is a handmade batik from Lasem of Central Java, Indonesia. Batik Lasem has unique charms and mostly bright colors because it is formed from the acculturation of Javanese and Chines. Batik Lasem is mostly produced in Lasem and it is sold in offline and online shops. This is a report on a project on community services from a collaboration between the author and one of the batik shops in Lasem. The project is creating a tutorial to introduce the making of handmade batik. The main tool and ingredients needed for making handmade batik are fabric, canting, wax, and coloring matter. The tutorial contains an explanation of equipment, material, and step by step on making handmade batik. The output of this project is a video and module for learning the basic of making handmade batik.
An Analysis of Meteorological Data in Sumatra and Nearby using Agglomerative Clustering Handhayani, Teny; Lewenusa, Irvan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5663

Abstract

Sumatra is one of the biggest and the second most crowded islands in Indonesia. Sumatra is also a place of abundance of tropical flora and fauna. This paper aims to cluster the cities in Sumatra and nearby based on the meteorology data. It implements Agglomerative hierarchical clustering and uses a daily time series dataset from 17 cities from 1 January 2010 to 31 December 2023. The dataset contains variables minimum temperature, maximum temperature, average temperature, humidity, sunshine duration, and average wind speed. The preprocessing data was dedicated to managing the missing values and data aggregation to create single-form data. The single-form data contains cities and meteorological variables used as an input for the clustering algorithm, i.e. K-Means, Fuzzy C-Means, K-Medoid, intelligent K-KMeans, and Agglomerative clustering. The Agglomerative clustering outperforms other methods (i.e. K-Means, Fuzzy C-Means, K-Medoid, and intelligent K-KMeans) and produces Silhouette scores of 0.11. The clusters are then analyzed to find their unique pattern. The cut-off when the number cluster is two, Agglomerative hierarchical clustering gathers Aceh, Sabang, Pekanbaru, Padang, and Padang Lawas in Cluster 1. Other cities, i.e., Nagan Raya, Batam, Jambi, Bandar Lampung, Medan, Pangkalpinang, Palembang, Bengkulu, Belitung, Tapanuli, Deli Serdang, and Nias are in Cluster 2. The results can be briefly explained that the characteristic of Cluster 1 has a higher average temperature, lower humidity, and lower sunshine duration than cities in Cluster 2. However, Cluster 1 has a lower average minimum temperature than Cluster 2. The pairs of cities which have the most similarities are (Aceh, Sabang), (Pekanbaru, Padang Lawas), (Nagan Raya, Nias), (Jambi, Palembang), (Bengkulu, Tapanuli), and (Medan, Deli Serdang). The annual trend in several cities shows that there exists an increasing trend in minimum temperature, rising sunshine duration, and decreasing wind speed. These are signs of climate change that need a proper handling.
PREDIKSI KEBANGKRUTAN PERUSAHAAN MENGGUNAKAN DECISION TREE, RANDOM FOREST DAN LOGISTIC REGRESSION: ANALISIS RASIO KEUANGAN SEBAGAI INDIKATOR RASIO Arya Dwi Saputra; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 2 (2025): 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.v13i2.34303

Abstract

Tujuan dari penelitian ini adalah untuk menggunakan tiga algoritma klasifikasi: Decision Tree, Random Forest, dan Logistic Regression untuk memprediksi kebangkrutan perusahaan. Sebagai indikator utama untuk mengukur risiko kebangkrutan perusahaan, penelitian ini menggunakan data rasio keuangan yang terdiri dari berbagai rasio keuangan, termasuk return on assets (ROA), margin laba operasi, dan total turnover aset. Penelitian menilai model yang dibangun menggunakan metrik performa seperti akurasi, ketepatan, recall, dan skor F1. Hasilnya menunjukkan bahwa model Logistic Regression memiliki tingkat akurasi tertinggi sebesar 96%. Penelitian ini memberikan wawasan tentang efektivitas rasio keuangan dalam memprediksi kebangkrutan dan relevansi penggunaan berbagai algoritma klasifikasi keuangan.
PERBANDINGAN KINERJA KNN, SVM, DAN ANN UNTUK MEMPREDIKSI LEVEL OBESITAS Georgia Sugisandhea; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 2 (2025): 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.v13i2.34306

Abstract

This study aims to compare the performance of K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Support Vector Machines (SVM), classification methods to find the best suited method to train a machine to classify someone to their group of obesity levels according to their eating habits and physical condition. This experiment uses the “Estimation of Obesity Levels based on Eating Habits and Physical Condition” dataset. The primary focus is on achieving high accuracy score and complex decision boundaries handling without minding the long training times, considering misclassification in the medical field might cause fatal consequences. This experiment’s result shows that the SVM classification method with linear kernel provides the best overall performance for classifying obesity level, with the average accuracy of 0.944, precision of 0.944, recall of 0.942, and f1-score of 0.942. Notably, with the help of C kernel parameter of 200, the model teaches near-perfect performance evaluation scores that has the result of 0.99 score in accuracy, precision, recall, and f1-score.
Co-Authors Adela Calista Adela Tania Agus Budi Dharmawan Akmal Farouqi Andre Andre Andre Andre, Andre Andrew Castello Purba Andrian, Gion Andry Winata Angelica Christina Arya Bintang Saputra Arya Dwi Saputra Arya Dwi Saputra Brando Dharma Saputra 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 Duncan Ariel Dwi Saputra, Arya Dyah Erny Herwindiati Ericko, Teddy Faradila Herfiyana Farhan Afrial Fawaz Gabriella Adeline Halim Georgia Sugisandhea Gion Andrian Hendryli, Janson Herfiyana, Faradila Huang, Jervis Irvan Lewenusa Irvan Lewenusa Irvan Lewenusa, Irvan Janson Hendryli Janson Hendryli Jason Jason Sunaryo Jaya, Jefri Jayadi, Bryan Valentino Jeanny Pragantha Jeanny Pragantha Jeanny Pragantha Jefri Jaya Jeremia Pinnywan Immanuel Jochsen, Erico 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 Manatap Sitorus Dolok Lauro Marcel Yusuf Rumlawang Arpipi Marchel Yusuf Rumlawang Arpipi Mathew Judianto Matthew Oni Matthew Russel Paul Mikael Reichi Sopany Mohammad Faraditya Eka Putra Monica Ong Muhammad Isnaini Syaifudin Naufal Firdausyan Nicholas Eugene Supardi Nicko Kurniawan Novario Jaya Perdana Oni, Matthew Owen Djoenaedi Owen Maytrio Phratama Paulus Samotana Zalukhu Peter James Tedja Phratama, Owen Maytrio Purba, Andrew Castello Raffy Sonata Sandy Permadi Sormin Sitorus Dolok Lauro , Manatap Sopany, Mikael Reichi Sumarlie , Devid Sumarlie, Aurellia Clearesta Tanudy, Clara Tasya Syamsudin Tommy Wijaya Putra Tony Tony Veri Wasino Wasino Wasino . Wasino Wasino William William Winata, Andry Yudistira Permana Zyad Rusdi