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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer EMITTER International Journal of Engineering Technology Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Knowledge Engineering and Data Science JICTE (Journal of Information and Computer Technology Education) Applied Information System and Management Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Information System,Graphics, Hospitality and Technology Teknika Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Insyst : Journal of Intelligent System and Computation Jurnal Nasional Teknik Elektro dan Teknologi Informasi Eduvest - Journal of Universal Studies Jurnal Inovasi Teknologi dan Edukasi Teknik Bulletin of Social Informatics Theory and Application
Esther Irawati Setiawan
Institut Sains Dan Teknologi Terpadu Surabaya
Aerospace Engineering Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Aplikasi Mobile Untuk Memantau Body Mass Index Dengan Metodologi Scrum Esther Irawati Setiawan; Hans Keven Budi Prakoso; Tjwanda Putera Gunawan; Endang Setyati; Joan Santoso
Teknika Vol 10 No 3 (2021): November 2021
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v10i3.405

Abstract

Pandemi berkepanjangan menyebabkan adanya kecenderungan manusia untuk kurang bergerak dan berolahraga, sehingga terjadi peningkatan berat badan yang menyebabkan penurunan kualitas kesehatan. Di samping itu, teknologi smartphone dewasa ini semakin berkembang pesat dan telah menjadi kebutuhan sehari-hari. Oleh karena itu, teknologi smartphone sebaiknya dimanfaatkan sebaik mungkin, sehingga dapat digunakan dalam berbagai aspek kehidupan, seperti penghitungan Body Mass Index (BMI), yang diharapkan dapat mengontrol tingkat tumbuhnya obesitas pada masyarakat terutama di masa pandemi ini. Pengembangan aplikasi ini mencakup penggunaan kamera dalam penghitungan BMI. Jika pada umumnya penghitungan BMI dilakukan dengan menggunakan tinggi dan berat badan, aplikasi ini dapat menggunakan gambar dari kamera smartphone sebagai sumber datanya. Melalui pembuatan aplikasi penghitungan BMI ini, dapat disimpulkan bahwa metodologi Scrum sangat membantu dalam proses pencatatan perkembangan kerja task-task pembuatan aplikasi saat mengerjakan setiap sprint mulai sprint pertama hingga empat. Penghitungan BMI dengan menggunakan hasil gambar dari kamera memiliki tingkat akurasi sebesar 70%.
Information Extraction Pada Berita Acara Pembagian Harta Waris Berdasarkan Hukum Islam Endang Setyati; Esther Irawati Setiawan; Arif Priyambodo
Teknika Vol 10 No 3 (2021): November 2021
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v10i3.415

Abstract

Hukum waris adalah salah satu hukum utama Islam di Indonesia. Jika dalam peninggalan harta waris muncul perselisihan dalam pembagiannya dan menimbulkan sengketa di antara pihak yang berkepentingan, maka harus diselesaikan di Pengadilan Agama. Berita acara merupakan catatan resmi persidangan yang memuat segala kejadian di sidang pengadilan sehubungan dengan perkara yang disusun oleh panitera. Dokumen berita acara persidangan berbentuk tidak terstruktur dan ketiadaan aplikasi pencarian informasi untuk mendapatkan kedudukan dalam keluarga akan memperlambat proses penyusunan putusan di pengadilan. Oleh karena itu, diperlukan sebuah penelitian Rule Based Information Extraction yang mampu melakukan ekstraksi dokumen berita acara untuk mendapatkan data inti yaitu nama ahli waris, kedudukan dalam keluarga, dan jenis harta muwaris. Tahap awal dari penelitian ini adalah pembuatan rule yang terdiri dari kata kunci, kata prefix, dan kata sufiks. Selanjutnya dilakukan tahap ekstraksi data seperti tokenisasi, case folding, dan penghapusan bilangan. Hasil dari ekstraksi adalah perolehan jenis harta muwaris. Proses selanjutnya adalah hitung proporsi, yang akan menghasilkan output berbentuk pohon keluarga beserta harta yang diterima oleh masing-masing ahli waris. Berdasarkan uji coba yang dilakukan, ketepatan akurasi bila dicocokkan dengan proses manual pada ekstraksi nama ahli waris dapat mencapai rata-rata 90,50%.
Detection of Banana and Its Ripeness Using Residual Neural Network Erwin Dhaniswara; Yosi Kristian; Esther Irawati Setiawan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.4844

Abstract

Automatic fruit detection utilizing computer vision techniques has been carried out to help the agriculture and plantation industries. This study researches smart systems to detect bananas and ripeness classification utilizing residual neural networks. The method used to detect bananas is transfer learning from pretraned Model VGG-19. Whereas, in the bananas ripeness classification process, residual neural networks, which are trained from the start, are used. Sliding Windows is used to detect the position of bananas followed by Non-Max Suppression to summarize the results of several detected bananas. Previous studies were limited to the level of ripeness, but in this study, bananas are detected and followed by the level of bananas ripeness (raw, ripe, and overripe). This study’s data uses bananas which were mixed with other kinds of fruit. There two kinds of bananas detection architecture used in this study, VGG-19 and Restnet. After they were used to detect bananas, it was found that VGG-19 was more suitable. The results of this study are very satisfying as it is seen from the bananas detection testing percentage using VGG-19 architecture which shows 100% ripe bananas, 99 % raw bananas, and 100% overripe bananas.Keywords: Detection of banana, banana ripeness, Non-Max suppression, residual block.
Indonesian Sentence Boundary Detection using Deep Learning Approaches Joan Santoso; Esther Irawati Setiawan; Christian Nathaniel Purwanto; Fachrul Kurniawan
Knowledge Engineering and Data Science Vol 4, No 1 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i12021p38-48

Abstract

Detecting the sentence boundary is one of the crucial pre-processing steps in natural language processing. It can define the boundary of a sentence since the border between a sentence, and another sentence might be ambiguous. Because there are multiple separators and dynamic sentence patterns, using a full stop at the end of a sentence is sometimes inappropriate. This research uses a deep learning approach to split each sentence from an Indonesian news document. Hence, there is no need to define any handcrafted features or rules. In Part of Speech Tagging and Named Entity Recognition, we use sequence labeling to determine sentence boundaries. Two labels will be used, namely O as a non-boundary token and E as the last token marker in the sentence. To do this, we used the Bi-LSTM approach, which has been widely used in sequence labeling. We have proved that our approach works for Indonesian text using pre-trained embedding in Indonesian, as in previous studies. This study achieved an F1-Score value of 98.49 percent. When compared to previous studies, the achieved performance represents a significant increase in outcomes..
Pembelajaran Ikatan Molekul Dalam Pelajaran Kimia Menggunakan Augmented Reality Honoris Setiahadi; Endang Setyati; Esther Irawati Setiawan
JICTE (Journal of Information and Computer Technology Education) Vol 1, No 2 (2017): October
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (588.749 KB) | DOI: 10.21070/jicte.v1i2.2086

Abstract

Merging two atoms that have valence electrons around them can be described but it is not an easy thing for high school students in the Natural Sciences (IPA) class. This study analyzes the effectiveness of Android-based Augmented Reality (SMARt) System Molecules technology on understanding molecular bonding material in chemistry lessons. The SMARt technology is able to provide a better understanding of molecular bonding material because there are 3D animation of elements and molecules with valence electrons surrounding it. The post test average value for the control class was 68.57 without using SMARt technology while the experimental class average value was 79.71 after using SMARt.
Analisis Pendapat Masyarakat terhadap Berita Kesehatan Indonesia menggunakan Pemodelan Kalimat berbasis LSTM Esther Irawati Setiawan; Adriel Ferdianto; Joan Santoso; Yosi Kristian; Gunawan Gunawan; Surya Sumpeno; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 1: Februari 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1263.215 KB) | DOI: 10.22146/jnteti.v9i1.115

Abstract

The uncertainty of health news content, which is spread on social media, raises the need for validation of the truth. One validation approach is to consider the opinion or attitudes of most people, which is called a stance on a topic, whether they support, oppose, or being neutral. This paper proposes a stance analysis model to classify the relationship between sentences so that it can recognize the correlation of the opinion of the writer in the headline of the problem claim. The proposed model uses several Long Short-Term Memory (LSTM), which represent the interrelationship of news for analysis of the relationship between a claim with other news. The formation of word representation vectors is carried out in conjunction with LSTM-based stance classification training. Sentence embedding is done to get the vector representation of sentences with LSTM. Each word in a sentence occupies one time-step in LSTM and the output of the last word is taken as a sentence representation. Based on the results of trials with the Indonesian health-related dataset that was built for this study, the proposed stance classification model was able to achieve an average F1-score value of 71%, with the supporting value 69%, opposing as much as 70%, and neutral 74%.
3D Visualization for Lung Surface Images of Covid-19 Patients based on U-Net CNN Segmentation FX Ferdinandus; Esther Irawati Setiawan; Eko Mulyanto Yuniarno; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.709

Abstract

The Covid-19 infection challenges medical staff to make rapid diagnoses of patients. In just a few days, the Covid-19 virus infection could affect the performance of the lungs. On the other hand, semantic segmentation using the Convolutional Neural Network (CNN) on Lung CT-scan images had attracted the attention of researchers for several years, even before the Covid-19 pandemic. Ground Glass Opacity (GGO), in the form of white patches caused by Covid-19 infection, is detected inside the patient’s lung area and occasionally at the edge of the lung, but no research has specifically paid attention to the edges of the lungs. This study proposes to display a 3D visualization of the lung surface of Covid-19 patients based on CT-scan image segmentation using U-Net architecture with a training dataset from typical lung images. Then the resulting CNN model is used to segment the lungs of Covid-19 patients. The segmentation results are selected as some slices to be reconstructed into a 3D lung shape and displayed in 3D animation. Visualizing the results of this segmentation can help medical staff diagnose the lungs of Covid-19 patients, especially on the surface of the lungs of patients with GGO at the edges. From the lung segmentation experiment results on ten patients in the Zenodo dataset, we have a Mean-IoU score = of 76.86%, while the visualization results show that 7 out of 10 patients (70%) have eroded lung surfaces. It can be seen clearly through 3D visualization.
Faktor-Faktor yang Mempengaruhi Repurchase Intention Pada E-Marketplace Dengan Menggunakan Extended Expectation Confirmation Model (ECM) Mohamad Fahmi Yusuf; Edwin Pramana; Esther Irawati Setiawan
Teknika Vol 12 No 1 (2023): Maret 2023
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v12i1.573

Abstract

E-marketplace saat ini telah berkembang sangat pesat. Pemerataan wilayah coverage dan kecepatan koneksi sudah jauh semakin berkembang, maka perubahan aktivitas dari tradisional menjadi digital pun semakin tinggi. UMKM di Indonesia mulai bermigrasi dari yang sebelumnya menjual produk secara konvensional di toko dan pasar, sekarang beralih secara online lewat media sosial maupun e-marketplace. Repurchase intention sangat penting dan sangat diinginkan karena untuk mendapatkan kepercayaan pelanggan baru dibutuhkan biaya yang besar. Proses mendapatkan pelanggan baru dan mengubahnya menjadi transaksi pembelian, lima kali lebih mahal daripada mempertahankan pelanggan yang sudah ada. Tujuan penelitian ini adalah untuk mengidentifikasi faktor-faktor yang mempengaruhi niat repurchase intention dalam e-marketplace dan untuk mengetahui hubungan antar faktor-faktor tersebut. Tahap awal penelitian ini adalah pengembangan model teoritis dan kuesioner, kemudian prosedur sampling & analisis data, dan interpretasi hasil & kesimpulan. Langkah pemrosesan data dimulai dengan factor analysis, reliability test, dan mencari nilai cronbach alpha. Selanjutnya dilakukan penggambaran model teoritis dengan AMOS dan dilakukan analisis SEM dari model construct ECM yang diberikan sehingga output yang didapatkan adalah hasil nilai standardized effect dan nilai magnitude. Kesesuaian penelitian ini dengan model penelitian yang diajukan dalam tabel Fit Statistic Model. Hasil penelitian menunjukkan terdapat enam faktor diterima yang mempengaruhi repurchase intention yaitu trust, confirmation, satisfaction, brand awareness, ease of use, dan electronic word of mouth, dan tiga hipotesis yang ditolak yaitu security terhadap repurchase intention & trust dan brand awareness terhadap repurchase intention.
Indonesian Language Term Extraction using Multi-Task Neural Network Joan Santoso; Esther Irawati Setiawan; Fransiskus Xaverius Ferdinandus; Gunawan Gunawan; Leonel Hernandez
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p160-167

Abstract

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two multilayer perceptron neural networks to perform Part-of-Speech (PoS) labeling and Noun Phrase Chunking. Our models were trained as a joint model to solve this problem. Our proposed method, with an f-score of 86.80%, can be considered a state-of-the-art algorithm for performing term extraction in the Indonesian Language using noun phrase chunking.
Aspect Based Sentiment Analysis Marketplace Product Reviews Using BERT, LSTM, and CNN Syaiful Imron; Esther Irawati Setiawan; Joan Santoso; Mauridhi Hery Purnomo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4751

Abstract

Bukalapak is one of the largest marketplaces in Indonesia. Reviews on Bukalapak are only in the form of text, images, videos, and stars without any special filters. Reading and analyzing manually makes it difficult for potential buyers. To help with this, we can extract this review by using aspect-based sentiment analysis because an entity cannot be represented by just one sentiment. Several previous research stated that using LSTM-CNN got better results than using LSTM or CNN. In addition, using BERT as word embedding gets better results than using word2vec or glove. For this reason, this study aims to classify aspect-based sentiment analysis from the Bukalapak marketplace with BERT as word embedding and using the LSTM-CNN method, where LSTM is for aspect extraction and CNN for sentiment extraction. Based on testing the LSTM-CNN method, it gets better results than LSTM or CNN. The LSTM-CNN model gets an accuracy of 93.91%. Unbalanced dataset distribution can affect model performance. With the increasing number of datasets used, the accuracy of a model will increase. Classification without using stemming on datasets can increase accuracy by 2.04%.
Co-Authors Adi Sutanto Adriel Ferdianto Aidil Aji Prasetya Wibawa Albertus Josef Andika Arif Priyambodo Arya Tandy Hermawan Christian Nathaniel Purwanto Dhaniswara, Erwin Edwin Pramana Edwin Pramana Effendy Hadi Sutanto Eko Mulyanto Yuniarno Endang Setyati F.X. Ferdinandus Fachrul Kurniawan Ferdinandus, F. X. Fitria Fitria Fujisawa, Kimiya Gunawan Gunawan Gunawan Halim, Kevin Jonathan Hans Juwiantho Hans Keven Budi Prakoso Harianto, Reddy Alexandro Harits Ar Rosyid Hartono, Luluk Setiawati Hartono, Patrick Heppi Siswanto Herman Budianto Herman Thuan To Saurik Honoris Setiahadi I Ketut Eddy Purnama Ika Lestari Imron, Syaiful Joan Santoso Johanes, Sugiharto Kimiya Fujisawa Kristian, Yosi Langgeng, Yudo Sembodo Hastoro Leonel Hernandez Lim, Ernest Lukman Zaman Machfudin, Mohammad Farid Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mohamad Fahmi Yusuf Muhammad Arief Rahman Mustaqin, Farhan Faisal Zainul Nagari, Widean Natalie, Valerynta Putra, Adi Surya Rachmatullah, Sholeh Rahmatullah, Dewangga Ramadhan Ramadhan Safitri, Lia Samuel Budi Wardhana Kusuma Santoso , Joan Saputra, Daniel Gamaliel Setya Ardhi Singh, Vrijraj Sugiono, Judi Prajetno Surya Sumpeno Syaiful Imron Syaiful Imron Titis Arwindarti Tjendika, Patrick Tjwanda Putera Gunawan Vu, Tong Nam Tuan Wardoyo, Nikko Riestian Putra Yosi Kristian Yuliana Yuliana YUNI YAMASARI Zaman, Lukman