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All Journal Jurnal Ilmu Komputer dan Informasi Transmisi: Jurnal Ilmiah Teknik Elektro JURNAL SISTEM INFORMASI BISNIS Jurnal Sistem Komputer Jurnal Teknik Elektro Journal of Telematics and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer CESS (Journal of Computer Engineering, System and Science) Edu Komputika Journal Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal Komputer Terapan J-SAKTI (Jurnal Sains Komputer dan Informatika) TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal Ilmu Keperawatan Komunitas Indonesian Journal of Electrical Engineering and Computer Science J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi) JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Jurnal Rekayasa elektrika
Dania Eridani
Departemen Teknik Komputer, Universitas Diponegoro, Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Library & Information Science Nursing

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Perancangan User Interface dan User Experience (UI/UX) pada Aplikasi Konek untuk PT. Agro Lestari Merbabu Berbasis Mobile dengan Menggunakan Metode Design Thinking Pratama, Andro Adhita; Prasetijo, Agung Budi; Eridani, Dania
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 1 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i1.72750

Abstract

Bidang pertanian adalah salah satu ujung tombak untuk menopang kehidupan manusia. Meskipun demikian, aktivitas mengakses informasi pertanian melalui media internet masih merupakan hal yang baru bagi petani. Terlihat dari kurangnya akses petani terhadap informasi mengenai pertanian. Hal inilah yang mendorong PT Agro Lestari Merbabu, untuk membantu mitra dalam menangani masalah "“ masalah pada tanaman yang mereka budidayakan. Bisa dengan mendapatkan solusi dari informasi yang dihadirkan pada aplikasi dan konsultasi kepada ahli tani. Penelitian ini bertujuan merancang antarmuka pengguna (UI) dan pengalaman pengguna (UX) sesuai dengan kebutuhan pengguna serta menghadirkan desain antarmuka yang baik untuk memberikan kepuasan kepada pengguna. Dalam pembuatan aplikasi tersebut, metode Design Thinking digunakan dengan berfokus pada desain antarmuka yang memudahkan bagi pengguna. Pengujian desain antarmuka dengan menggunakan metode usability testing cognitive walkthrough yang berfokus pada aspek learnability, effectiveness, dan satisfaction. Pada tahap penilaian satisfaction melibatkan pengujian usability menggunakan System Usability Scale (SUS) sebagai parameter evaluasi tingkat kepuasan pengguna terhadap antarmuka. Pada akhir penelitian desain prototype telah berhasil dirancang dengan menggunakan Figma. Hasil penilaian dari pengguna terhadap aplikasi memperoleh skor rata-rata sebesar 77 dengan predikat "good" untuk Pengguna atau Mitra, 83 untuk ahli tani dengan predikat "good", dan 87.5 dengan predikat "excellent" untuk admin. Oleh karena itu, antarmuka aplikasi Konek telah mendapatkan kategori "acceptable" mengindikasikan desain antarmuka baik dan diterima oleh pengguna.
Analisis Purwarupa Sistem Otomatisasi Penerang Jalan Untuk Menghemat Daya Listrik Eridani, Dania; Asyauqqi, M. Azka; Prasetijo, Agung Budi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 1 (2020): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (794.721 KB) | DOI: 10.30645/j-sakti.v4i1.194

Abstract

Public street lighting automation is one part of an area or a city development design. The design of public street lightning need to pay attention to several aspects, where one of them is how the public street lighting design is able to save electricity consumption. This research contain several combination of public street light-ing automation designs. It use Arduino Mega 2560 board as the main control board, KY-018 sensor as the sen-sor to detect the environment light intensity, HC-SR501 sensor as the sensor to detect object movement, AC light dimmer as the regulator of the lamp, AC power meter as a measuring device to measure the power con-sumption of the lamp used. The testing mechanism are consist of testing the input and output components in the system and testing the electricity consumption in the circuit design. The results of the research carried out, showed a comparison of the level of electricity consumption from each series of combinations that can be used as a reference for road lighting development planning.
Analisis Purwarupa Sistem Otomatisasi Penerang Jalan Untuk Menghemat Daya Listrik Eridani, Dania; Asyauqqi, M. Azka; Prasetijo, Agung Budi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 1 (2020): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v4i1.194

Abstract

Public street lighting automation is one part of an area or a city development design. The design of public street lightning need to pay attention to several aspects, where one of them is how the public street lighting design is able to save electricity consumption. This research contain several combination of public street light-ing automation designs. It use Arduino Mega 2560 board as the main control board, KY-018 sensor as the sen-sor to detect the environment light intensity, HC-SR501 sensor as the sensor to detect object movement, AC light dimmer as the regulator of the lamp, AC power meter as a measuring device to measure the power con-sumption of the lamp used. The testing mechanism are consist of testing the input and output components in the system and testing the electricity consumption in the circuit design. The results of the research carried out, showed a comparison of the level of electricity consumption from each series of combinations that can be used as a reference for road lighting development planning.
YoloV8, EfficientNetv2, and CSP Darknet Comparison as Recognition Model’s Backbone for Drone-Captured Images Kridalukmana, Rinta; Eridani, Dania; Septiana, Risma; Windasari, Ike Pertiwi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2880

Abstract

Artificial intelligence (AI) has recently empowered drones to support smart city apps and recognize on-the-ground objects or events. Various pre-trained backbones are available to develop object recognition models, and some of them could boost the models’ accuracy. Consequently, it becomes difficult for practitioners to select a suitable backbone as a feature extractor during recognition model development. Hence, this research aims to provide a benchmark examining the performance of three popular backbones in supporting recognition models using images captured by drones as the dataset. This research used the UAV-AUAIR dataset and compared three deep learning backbone architectures as the feature extractor, namely YoloV8_s, EfficientNetv2_s, and CSP_DarkNet_l. The head part of each selected backbone was replaced with YoloV8Detector architecture, provided by Keras-CV, to perform the inference tasks. The models generated during training were evaluated against four measurement methods: loss function, intersection over union (IOU), across-scale mean average precision (mAP), and computational performance. The results showed that the model generated using EfficientNetv2_s backbone outperformed the others in most criteria, except the computational performance and detecting small objects, which was won by YOLOV8_s and CSP_Darknet_l, respectively. Thus, EfficientNetv2_s and CSP_DarkNet_l can be considered if app development concerns accuracy. Meanwhile, YoloV8_s is far better when computational performance is essential, as its prediction time achieved 0.8 seconds per image. This study is essential as a reference for practitioners, particularly those who want to develop an object-recognition model based on a pre-trained backbone.
Sistem Isyarat Bahasa Indonesia (SIBI) Metode Convolutional Neural Network Sequential secara Real Time Nurhayati, Oky Dwi; Eridani, Dania; Tsalavin, Muhammad Hafiz
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 4: Agustus 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022944787

Abstract

Bahasa isyarat dengan menggunakan gerakan tangan biasanya dilakukan oleh tuna rungu dan tuna wicara. Bahasa isyarat yang digunakan di Indonesia adalah SIBI (Sistem Isyarat Bahasa Indonesia). Namun, penggunaan bahasa isyarat tangan tidak selalu di mengerti oleh manusia normal sehingga dibutuhkan perangkat tambahan yang dapat mempermudah dalam menerjemahkan suatu isyarat. Perangkat tambahan yang dikembangkan dalam penelitian ini melibatkan teknologi visi komputer deep learning sehingga menghasilkan tools untuk menerjemahkan bahasa isyarat tangan. Dalam penelitian ini, gambar isyarat tangan di capture menggunakan webcam kemudian dilakukan pre-processing dengan mengubah gambar ke dalam bentuk HSV. Gambar yang digunakan dalam penelitian berupa citra sebanyak 26 kelas huruf alfabet SIBI dan 3 kelas tambahan, dengan masing-masing kelas memiliki 1000 gambar. Kemudian dilakukan cropping dan thresholding dengan menempatkan isyarat tangan yang berbentuk huruf  kedalam kotak yang merupakan area ROI untuk memudahkan pengenalan. Teknologi visi komputer deep learning convolutional neural network (CNN) digunakan untuk feature learning dan mengklasifikasi isyarat tangan pada sebuah obyek. Untuk menguji metode CNN, digunakan berbagai variasi cahaya sebesar 10-200 lux, serta jarak dari tangan ke webcam 50-200 cm. Hasil penelitian dengan metode CNN pada citra isyarat tangan memberikan akurasi sebesar 92%, presisi 91,96%, sensitivitas 91,9%, spesivisitas 91,96% dan f1 score 91,9%. AbstractSign language is usually used by deaf and speech impaired persons. The Sistem Isyarat Bahasa Indonesia (SIBI) is a hand signal language used in Indonesia. The use of hand signals is not always understood by normal humans, such that additional devices are needed to make sign translation easier. The additional device in this study is developed using deep learning and computer vision technology to produce a hand signal translation tool. This study uses 29 sign images for a dataset, consisting of 26 letters of the alphabet and 3 additional signs, namely space, delete, and unclassified. Pre-processing is performed by converting the image into HSV, cropping, and thresholding to make easy recognition. The convolutional neural network (CNN) method is then used as a learning feature and hand signals classifier on an object. The testing phase is performed on various lights ranging from 10-200 lux and the hand distance to the webcam is about 50-200 cm. Experimental results show that the CNN method on the hand signal image could provide an accuracy of 97.2%, precision of 91.96%, sensitivity of 91.9%, specificity of 91.96%, and F1 score of 91.9%, respectively.
A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent Kridalukmana, Rinta; Eridani, Dania; Septiana, Risma
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i1.1199

Abstract

Predicting immediate future actions taken by an intelligent agent is considered an essential problem inhuman-autonomy teaming (HAT) in many fields, such as industries and transportation, particularly toimprove human comprehension of the agent as their non-human counterpart. Moreover, the results of suchpredictions can shorten the human response time to gain control back from their non-human counterpartwhen it is required. An example case of HAT that can be benefitted from the action predictor is partiallyautomated driving with the autopilot agent as the intelligent agent. Hence, this research aims to develop anapproach to predict the immediate future actions of an intelligent agent with partially automated drivingas the experimental case. The proposed approach relies on a machine learning method called naive Bayesto develop an action classifier, and the Dynamic Bayesian Network (DBN) as the action predictor. Theautonomous driving simulation software called Carla is used for the simulation. The results show that theproposed approach is applicable to predict an intelligent agent’s three-second time-window immediate futureaction.
PERANCANGAN PROTOTYPE ALAT MONITORING DAN APLIKASI EDUKASI TANAMAN GUNA KONSERVASI HUTAN BERBASIS PWA MENGGUNAKAN ALGORITMA CNN Khanuun Maulida Puspita Hasyim; Daniel Alvaro Sormin; Samuel Miduk Anugrah Pasaribu; Dania Eridani; Arseto Satriyo Nugroho
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 2 No. 3 (2024): Juli : Jurnal Ilmiah Sains Teknologi dan Informasi
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v2i3.739

Abstract

Perkembangan teknologi membawa perubahan pada bidang edukasi, termasuk pada bidang edukasi lingkungan. Salah satu komunitas yang bergerak di bidang lingkungan yaitu Jepara Green Generation (Jegeg). Jegeg membutuhkan aplikasi yang memiliki fitur edukasi baik dalam bentuk artikel maupun video, fitur pengenalan tanaman, tempat wisata, tempat konservasi, serta informasi terkait kegiatan peduli lingkungan. Sehingga diperlukan suatu inovasi teknologi sebagai pemantik untuk meningkatkan kepekaan dan kesadaran masyarakat dengan keadaan lingkungan alam sekitarnya. Oleh karena itu, dibuatlah aplikasi Forestrytech yang menggunakan metode design thinking dan pengujian SEQ dan SUS dalam perancangan antarmuka. Aplikasi ini bertujuan untuk menyediakan pemantauan hutan secara real-time yang dapat diakses oleh masyarakat. ForestryTech, sebuah aplikasi pemantauan dan edukasi konservasi hutan berbasis PWA menggunakan algoritma CNN. Selain itu, juga dibuat prototipe alat monitoring hutan berbasis IoT menggunakan ESP32 yang dapat dipantau dan diakses melalui aplikasi. Sistem ini dapat membaca nilai suhu, kelembapan, tekanan udara, api, asap, dan kecepatan angin dengan akurasi yang baik.
Co-Authors Agung Budi Prasetijo Agung Budi Prasetijo, Agung Budi Al Arthur Faizal Arseto Satriyo Nugroho Asyauqqi, M. Azka Danal Meizantaka Daeanza Daniel Alvaro Sormin Dea Ismy Soraya Isabella Eko Didik Widianto Fidarliyan, Yarnish Dwi Sagita Handayani, Fitria Ike Pertiwi Windasari Isnanto, Erizal Isnanto, Erizal Ivan Sanusi Khanuun Maulida Puspita Hasyim Kurniawan Teguh Martono M Arfan Michael SM Pakpahan Muhamad Y. Dias Muhammad Risqullah Naufal Yudanar Nadia Febrianita Gunarto Najich, Zakky Ainun Nur Kholid Oky Dwi Nurhayati Paulus Insap Santosa Pratama, Andro Adhita Richard Dwi Olympus Augustinus Ridi Ferdiana Rifki, Muhammad Aditya Miftahul Rifki, Muhammad Aditya Miftahul Rinta Kridalukmana Risma Septiana Rita Hadi Widyastuti Samuel Miduk Anugrah Pasaribu Santoso, Imam Tsalavin, Muhammad Hafiz Ulinuha, Ajik Wildan Budi Bawono Yuli Christyono