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All Journal Nirmana Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Artificial Intelligence Research Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jiko (Jurnal Informatika dan komputer) Jurnal Teknik Informatika (JUTIF)
Sigit Adinugroho
STMIK Amikom Yogyakarta
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mechanical Engineering

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Designing Interactive Digital Media for Jakarta's Historical Sites as a Medium for Conservation and Placemaking Adinugroho, Sigit; Mutiaz, Intan Rizky
Nirmana Vol 14, No 1 (2012): JANUARY 2012
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (303.436 KB) | DOI: 10.9744/nirmana.14.1.36-46

Abstract

Sejarah Jakarta terbentang mulai dari abad ke-4 Masehi, mengalami perkembangan pesat pada masa pemerintahan Belanda antara abad ke-17 hingga 19 Masehi, dan mewarisi Republik Indonesia dengan peninggalan-peninggalan infrastruktur serta kultural di kota ini. Kota yang dulunya disebut Batavia ini merupakan pusat pemerintahan dan ekonomi yang penting bagi Belanda. Signifikansi situs-situs historis Jakarta sebagai “museum hidup” di masa kini tidak sejalan dengan keadaannya yang terancam punah. Ada situs yang dilindungi, ada yang dikonversi dan sisanya dibiarkan saja. Wilayah Kota Tua mengandung nilai-nilai historis paling kental di antara semua tempat di Jakarta, menurut observasi, tidak terawat dan kurang layak dijadikan objek wisata. Media digital interaktif berpotensi untuk membantu menyajikan informasi, mencapai audiens dan memberikan edukasi pada masyarakat. Penelitian ini berusaha untuk mengkaitkan masalah konservasi situs historis Jakarta dengan spekstrum media interaktif. Ia dimulai dengan riset kualitatif mengenai konservasi, sejarah dan desain produk digital, dilengkapi wawancara dengan ahli sejarah dan observasi langsung ke lapangan. Proses iterasi cepat digunakan dalam fase perancangan dan pengembangan. Hasilnya adalah sebuah konsep aplikasi interaktif digital portabel yang memandu pengunjung pada situs-situs sejarah di Jakarta.
Movie recommender systems using hybrid model based on graphs with co-rated, genre, and closed caption features Adikara, Putra Pandu; Sari, Yuita Arum; Adinugroho, Sigit; Setiawan, Budi Darma
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 7, No 1 (2021): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v7i1.2081

Abstract

A movie recommendation is a long-standing challenge. Figuring out the viewer’s interest in movies is still a problem since a huge number of movies are released in no time. In the meantime, people cannot enjoy all available new releases or unseen movies due to their limited time. They also still need to choose which movies to watch when they have spare time. This situation is not good for the movie business too. In order to satisfy people in choosing what movies to watch and to boost movie sales, a system that can recommend suitable movies is required, either unseen in the past or new releases. This paper focuses on the hybrid approach, a combination of content-based and collaborative filtering, using a graph-based model. This hybrid approach is proposed to overcome the drawbacks of combination in the content-based and collaborative filtering. The graph database, Neo4j is used to store the collaborative features, such as movies with its genres, and ratings. Since the movie’s closed caption is rarely considered to be used in a recommendation, the proposed method evaluates the impact of using this syntactic feature. From the early test, the combination of collaborative filtering and content-based using closed caption gives a slightly better result than without closed caption, especially in finding similar movies such as sequel or prequel.
NUTRITION ESTIMATION OF LEFTOVER USING IMPROVED FOOD IMAGE SEGMENTATION AND CONTOUR BASED CALCULATION ALGORITHM Adinugroho, Sigit; Sari, Yuita Arum; Maligan, Jaya Mahar; Sari, Kartika; Bihanda, Yusuf Gladiensyah; Nuraini, Nabila; Fatchurrahman, Danial
Journal of Environmental Engineering and Sustainable Technology Vol 9, No 01 (2022)
Publisher : Directorate of Research and Community Service (DRPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jeest.2022.009.01.5

Abstract

In pandemic conditions, awareness of keeping a healthy balance is necessary. One is considering food consumption and understanding its nutrition content to avert food waste. We have been developing a prototype to estimate the nutrition of leftover food, and the main problem lies in image segmentation. Therefore, we propose the Improved Food Image Segmentation (IFIS) and Contour Based Calculation (CBC) to measure the area of the segmented image instead of pixel-wise. First, the tray box image is acquired and broken down into compartments using an automated cropping algorithm. The first step of this proposed method is tray box image acquisition and dividing the compartment using an automatic cropping algorithm. Then each compartment is treated using IFIS, calculates the result of IFIS by CBC, measures the estimated leftover by Automatic Food Leftover Estimation (AFLE), and then predicts the nutritional content. The evaluation is applied by comparing the actual measurement from the Comstock method and leftover estimation by the proposed algorithm. The result shows that Root Square Means Error (RMSE) reaches 0.48 compared to the actual weighing scale and 96.67% accuracy compared to the Comstock method. Based on the results, the proposed algorithm is sufficient to be applied.
SPERM ABNORMALITY CLASSIFICATION USING MULTI-PURPOSE IMAGE EMBEDDING AND CLASSICAL MACHINE LEARNING Adinugroho, Sigit; Sari, Yuita Arum; Kurniawan, Wijaya; Arwan, Achmad
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.8938

Abstract

Since sperm cells have big impact for human welfare in terms of reproduction, there are many studies have been done. In this case, we are attracted to enrich the method in determining the morphological properties of them using machine learning. Most study about it is done using 2-steps action that are feature extraction which is continued by classification. In our work, we aimed to lower the complexity by using image embedding as a general-purpose feature extractor that requires no training. For feature extraction using image, it is found that RGB has better performance compared to grayscale if we want to use Support Vector Machine (SVM). Meanwhile, when a comparation is done between SVM, random forest, Multi-Layer Perceptron (MLP), Naïve Bayes, and k-Nearest Neighbour (kNN) for classification process, MLP shows the best performance among them which is around 85%. Moreover, our proposed method has low complexity indicated by the training time around one and a quarter minute s for the most accurate method, compared to hours of training time in similar methods.
Evaluasi Komparatif Arsitektur Lightweight CNN, MobileNetV2, dan EfficientNetB0 dalam Deteksi Penyakit Daun Jagung Artha Sitorus, Yoshua; Arum Sari, Yuita; Adinugroho, Sigit
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 8 (2025): Agustus 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Penyakit daun jagung seperti Hawar Daun, Karat Umum, dan Bercak Daun Abu-abu menjadi ancaman serius bagi ketahanan pangan. Keterbatasan deteksi konvensional mendorong perlunya solusi teknologi yang efisien untuk perangkat bergerak. Penelitian ini bertujuan mengevaluasi secara komparatif arsitektur CNN lightweight, MobileNetV2 dan EfficientNetB0, untuk deteksi penyakit daun jagung. Metode yang digunakan adalah transfer learning pada dataset publik berisi 4.188 citra daun jagung (empat kelas). Hasil pengujian menunjukkan keunggulan signifikan MobileNetV2 yang mencapai akurasi 91,73% (presisi, recall, F1-score rata-rata 0,92). Sebaliknya, arsitektur EfficientNetB0 mengalami kegagalan konvergensi dengan akurasi hanya 31,21%, di mana model mengalami kolaps prediksi. MobileNetV2 juga terbukti lebih efisien dengan 2,26 juta parameter dibandingkan 4,05 juta pada EfficientNetB0. Disimpulkan bahwa MobileNetV2 menawarkan keseimbangan terbaik antara akurasi dan efisiensi, sehingga direkomendasikan untuk pengembangan aplikasi diagnosis penyakit daun jagung pada perangkat mobile.
Deteksi Orientasi Wajah Minim Pencahayaan Pada Kursi Roda Pintar Menggunakan MediaPipe, CLAHE Dan Metode RBR Haikal, M. Fikri; Utaminingrum, Fitri; Adinugroho, Sigit
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 8 (2025): Agustus 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Mobilitas merupakan kebutuhan dasar setiap individu, termasuk bagi penyandang disabilitas yang hanya mampu menggerakkan bagian atas tubuhnya seperti pasien penyakit tetraplegia. Dalam menunjang kemandirian mereka, dibutuhkan sistem navigasi kursi roda tanpa ketergantungan pada tubuh bagian bawah. Penelitian ini mengembangkan sistem kendali kursi roda pintar berbasis orientasi wajah menggunakan MediaPipe Face Mesh dan klasifikasi berbasis aturan (Rule-Based Reasoning). Sistem deteksi orientasi wajah memanfaatkan tiga titik landmark wajah, yaitu titik pada hidung (ID 1) serta dua titik terluar mata kiri (ID 33) dan kanan (ID 263). Landmark tersebut digunakan untuk menghitung dua parameter utama, yaitu sudut hidung dan eye-baseline. Proses klasifikasi arah gerak kursi roda menggunakan pendekatan RBR yang menetapkan aturan berbasis nilai ambang sudut untuk menentukan enam perintah navigasi: maju, berhenti, belok kanan, belok kiri, belok kanan tajam, dan belok kiri tajam. Sistem menggunakan metode CLAHE (Contrast Limited Adaptive Histogram Equalization) untuk meningkatkan citra gambar pada kondisi minim pencahayaan. Hasil pengujian menunjukkan bahwa penerapan CLAHE meningkatkan akurasi sistem dari 83,32% menjadi 96,29%, dengan tambahan waktu komputasi rata-rata 13,23%. Validasi klasifikasi pada pengujian akurasi deteksi landmark dilakukan dengan data pitch dan roll dari sensor gyroscope. Hasil penelitian ini menunjukkan bahwa metode CLAHE efektif dalam meningkatkan kinerja sistem tanpa membebani prosesor secara signifikan.
A Comparative Analysis of Color Channel-Based Feature Extraction using Machine Learning versus Deep Learning for Food Recognition Sari, Yuita Arum; Nugraha, Dwi Cahya Astria; Adinugroho, Sigit
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5001

Abstract

Automated Dietary Assessment Accurate food recognition is a big challenge in computer vision which is critical for developing Automated Dietary assessment and health monitoring systems. The key question it answered was whether traditional machine learning with feature engineering by hand can beat modern deep learning approaches? In this Context, this study serves as a comparative analysis of these two paradigms. The baseline method worked by extracting texture (LBP,GLCM) and color information from different channels of five colors spaces (RGB, HSV, LAB, YUV,YCbCr) followed by feeding these features into multiple classifiers such as Nearest Neighbor(NN), Decision Tree and Naïve Bayes. These were then compared to deep learning models (MobileNet_v2, ResNet18, ResNet50, EfficientNet_B0). The best traditional one can reach an accuracy of 93.33%, using texture features extracted from the UV channel and classified with a NN. Nevertheless, the deep learning models consistently presented higher performance and MobileNet_v2 reached up to 94.9% accuracy without requiring manual feature selection. In this paper, we show that end-to-end deep learning models are more powerful and error robust for food recognition. These results highlight their promise for constructing more effective and scalable real-world applications with less need for intricate, domain-specific feature engineering.
Improving Direct Image Regression for Blood Cell Enumeration with a Fine-Tuned Backbone Adinugroho, Sigit; Sari, Yuita Arum; Utaminingrum, Fitri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5004

Abstract

Complete blood count (CBC) examination provides an important insight for diagnosis or disease treatment. Currently, CBC examination requires complex and expensive devices that limit their deployment in remote area. The development of computer vision based method offers simplification to the process. However, its implementation is limited to the availability of large size labelled dataset. This research aims to develop a direct image regressor that is able to regress directly from image. There are two stages in estimation process. First, the backbone is trained using large dataset available for blood cell classification problem. Then the trained backbone is plugged into the final model by adding a fully connected neural network that acts as regressor. The whole model is then trained using limited whole blood cell count dataset. The evaluation process shows that training the backbone using large size related dataset improve the performance by 50%. This study can be used to create a low-cost blood component evaluation tool, particularly in rural areas where access to advanced laboratory equipment is limited.
Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction Sari, Yuita Arum; Hapsani, Anggi Gustiningsih; Adinugroho, Sigit; Hakim, Lukman; Mutrofin, Siti
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3183.967 KB) | DOI: 10.29099/ijair.v4i2.165

Abstract

Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor  (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.
Pencarian Produk yang Mirip Melalui Automatic Online Annotation dari Web dan Berbasiskan Konten dengan Color Histogram Bin dan Surf Descriptor Adikara, Putra Pandu; Adinugroho, Sigit; Sari, Yuita Arum
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 1: Februari 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (607.144 KB) | DOI: 10.25126/jtiik.201851630

Abstract

Banyaknya situs e-commerce memberikan kemudahan bagi pengguna yang ingin mencari dan membeli suatu produk, misalnya membeli makanan, obat, alat elektronik, kebutuhan sehari-hari, dan lain-lain. Pencarian suatu produk terhadap beberapa situs e-commerce akan menjadi sulit karena banyaknya pilihan situs, banyaknya penjual (merchant/seller) yang menjual barang yang sama, dan waktu yang lama karena harus berpindah-pindah situs hingga menemukan produk yang diinginkan. Selain itu dengan adanya teknologi smartphone berkamera, augmented reality, query pencarian bisa jadi hanya berupa citra, namun pencarian produk dengan menggunakan citra pada umumnya tidak diakomodasi di situs e-commerce. Dalam penelitian ini dikembangkan sistem meta search-engine yang menggunakan query berupa citra dan berbasiskan konten untuk menggabungkan hasil pencarian dari beberapa situs e-commerce. Citra query yang tidak diketahui namanya dibangkitkan tag atau kata kuncinya melalui Google reverse image search engine. Kata kunci ini kemudian diberikan ke masing-masing situs e-commerce untuk dilakukan pencarian. Fitur yang digunakan dalam pencocokan query dengan produk adalah fitur tekstual, color histogram bin, dan keberadaan citra objek yang dicari menggunakan SURF descriptor. Fitur-fitur ini digunakan untuk menentukan relevansi terhadap hasil penelusuran. Sistem ini dapat memberikan hasil yang baik dengan precision@20 dan recall hingga 1 dengan rata-rata precision@20 dan recall masing-masing sebesar 0,564 dan 0,608, namun juga bisa gagal dengan precision@20 dan recall sebesar 0. Hasil yang kurang baik ini dikarenakan tag yang dibangkitkan terlalu umum dan situs e-commerce-pun memberikan hasil yang umum juga
Co-Authors Achmad Arwan Artha Sitorus, Yoshua Arum Sari, Yuita Azhar, Naziha Azizah, Rizky Adinda Bihanda, Yusuf Gladiensyah Budi Darma Setiawan Fatchurrahman, Danial Firmansyah, Ilham Fitra Abdurrachman Bachtiar Haikal, M. Fikri Hapsani, Anggi Gustiningsih Intan Rizky Mutiaz Jaya Mahar Maligan Jayawarsa, A.A. Ketut Lukman Hakim Nugraha, Dwi Cahya Astria Nuraini, Nabila Putra Pandu Adikara Siti Mutrofin Utaminingrum, Fitri Wijaya Kurniawan Yuita Arum Sari