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All Journal Jurnal Teknologi dan Manajemen Informatika TEKNOLOGI: Jurnal Ilmiah Sistem Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer INTEGER: Journal of Information Technology Teknika: Engineering and Sains Journal Knowledge Engineering and Data Science JICTE (Journal of Information and Computer Technology Education) SMARTICS Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Konvergensi Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science Antivirus : Jurnal Ilmiah Teknik Informatika Journal of Information System,Graphics, Hospitality and Technology Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Teknika Teknika Journal of Electrical Engineering and Computer (JEECOM) Best : Journal of Applied Electrical, Science and Technology Insyst : Journal of Intelligent System and Computation J-Intech (Journal of Information and Technology) Joutica : Journal of Informatic Unisla Jurnal Nasional Teknik Elektro dan Teknologi Informasi JOINCS (Journal of Informatics, Network, and Computer Science) Insand Comtech : Information Science and Computer Technology Journal Jurnal Indonesia Sosial Teknologi JEECS (Journal of Electrical Engineering and Computer Sciences) Eksplorasi Teknologi Enterprise & Sistem Informasi (EKSTENSI) EduTech Journal

Endang Setyati

Institut Sains Dan Teknologi Terpadu Surabaya

Author-ID : 487621

Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences 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

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

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%.

Utilization Of Augmented Reality In Automotive Subjects For Basic Competencies Of Four-Wheeled Vehicle Brake Systems Muhammad Farkhan; Endang Setyati; Francisca Haryanti Chandra
BEST Vol 3 No 2 (2021): BEST
Publisher : Program Studi Teknik Elektro Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/best.vol3.no2.4243

In automotive learning, teachers generally use books and teaching aids as learning media. Automotive learning outcomes show the low value of learning outcomes. Thus a learning media is needed that can help improve learning outcomes. One way to overcome this problem is to use learning media that utilize augmented reality technology. In this study, a learning media using augmented reality technology based on android was developed to simulate the brake system on four-wheeled vehicles in 3 dimensions. The Augmented Reality work system used is marker based tracking, and uses 3D Max software and the Vuforia plug-in. In terms of pedagogy, this learning system uses the Modality Principle. Participants are class XI students of SMK YPM 4 Taman. This research uses experimental research. The students involved were 44 students divided into 2 groups, with each group consisting of 22 students. Both groups received a pre-test and a post-test. The experimental group was given treatment with Augmented Reality-based learning media, while the control group did not use conventional learning media. After making comparisons, the results show less than optimal due to the pandemic period. The results showed that the pre-test result between the control group and the experimental group was 49.32, and the post-test result for the control group was 62.73, while for the experimental group it was 73.18. So that from the difference in the difference in post-test scores between the experimental group and the control group shows that the treatment factor by providing Augmented Reality-based learning media in the experimental group has an influence. From observations and interviews, students were more active in learning activities and students were eager to take part in learning. This proves that students are interested in this media which can generate motivation to learn.

CNN based Face Recognition System for Patients with Down and William Syndrome Endang Setyati; Suharyono Az; Subroto Prasetya Hudiono; Fachrul Kurniawan
Knowledge Engineering and Data Science Vol 4, No 2 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i22021p138-144

Down syndrome, also known as trisomy genetic condition, is a genetic disorder that affects many people. Williams syndrome is a hereditary disorder that can affect anyone at birth. It marks medical and cognitive issues, such as cardiovascular illness, developmental delays, and learning impairments. This is accompanied by exceptional verbal abilities, a gregarious attitude, and a passion for music. Down syndrome and William Syndrome are both genetic illnesses. However, it can be distinguished from the arrangement of chromosome 21. Down syndrome and William syndrome can also be identified by recognizing faces, or facial characteristics, such as observing particular facial features. Therefore, this research develops Convolutional Neural Network (CNN) architectures to recognize Down syndrome and William syndrome using a facial recognition approach. A total of 480 facial photos were used in the study, with 390 images used for training data and 90 images used for testing data. The identification class is divided into three categories, Down syndrome, William syndrome, and normal. There are 160 photos in each patient class. This research presents two CNN architectures using a grayscale image of 256×256 pixels. The first CNN architecture comprises 12 layers, while the second comprises 15 layers. The average accuracy results with 12 layers were 91% by attempting to train and test six times. With 15 layers, the average accuracy value is 89%. In comparison, the first architecture has the highest accuracy value.

Digit Classification of Majapahit Relic Inscription using GLCM-SVM Tri Septianto; Endang Setyati; Joan Santoso
Knowledge Engineering and Data Science Vol 1, No 2 (2018)
Publisher : Universitas Negeri Malang

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

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

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.

HIDDEN MARKOV MODELS BASED INDONESIAN VISEME MODEL FOR NATURAL SPEECH WITH AFFECTION Endang Setyati; Mauridhi Hery Purnomo; Surya Sumpeno; Joan Santoso
Jurnal Ilmiah Kursor Vol 8 No 3 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i3.61

In a communication using texts input, viseme (visual phonemes) is derived from a group of phonemes having similar visual appearances. Hidden Markov model (HMM) has been a popular mathematical approach for sequence classification such as speech recognition. For speech emotion recognition, a HMM is trained for each emotion and an unknown sample is classified according to the model which illustrate the derived feature sequence best. Viterbi algorithm, HMM is used for guessing the most possible state sequence of observable states. In this work, first stage, we defined system of an Indonesian viseme set and the associated mouth shapes, namely system of text input segmentation. The second stage, we defined a choice of one of affection type as input in the system. The last stage, we experimentally using Trigram HMMs for generating the viseme sequence to be used for synchronized mouth shape and lip movements. The whole system is interconnected in a sequence. The final system produced a viseme sequence for natural speech of Indonesian sentences with affection. We show through various experiments that the proposed, the results in about 82,19% relative improvement in classification accuracy.

Model CNN LeNet dalam Rekognisi Angka Tahun pada Prasasti Peninggalan Kerajaan Majapahit Tri Septianto; Endang Setyati; Joan Santoso
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 3, Year 2018 (July 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

The object of the inscription has a feature that is difficult to recognize because it is generally eroded and faded. This study analyzed the performance of CNN using LeNet model to recognize the object of year digit found on the relic inscriptions of Majapahit Kingdom. Object recognition with LeNet model had a maximum accuracy of 85.08% at 10 epoch in 6069 seconds. This LeNet's performance was better than the VGG as the comparison model with a maximum accuracy of 11.39% at 10 epoch in 40223 seconds.

Expert System untuk Mendeteksi Penyakit Gigi Menggunakan Shell e2gLite dari Expertise2go Tuesday saka gustaf; Joan Santoso; Endang Setyati
Journal of Electrical Engineering and Computer (JEECOM) Vol 2, No 2 (2020)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v2i2.1442

Jumlah tenaga kerja kesehatan gigi di kalangan masyarakat dinilai kurang seimbang.Tujuan utama penelitian ini untuk mendeteksi penyakit pada gigi seseorang dengan menyediakan solusi berupa sistem pakar . Tahapan proses pada penelitian ini meliputi: Pembelajaran literatur mengenai Forward Chaining ,Certainly Factor dan software expertise2Go yaitu E2glite dan Pemahaman penerapan metode Certainly Factor , Menentukan batasan-batasan permasalahan dari penelitian mengenai penyakit pada gigi serta merumuskan serangkaian solusi-solusi berupa informasi untuk mengatasi permasalahan penyakit pada gigi.Nilai akurasi dihitung dari tingkat keberhasilan sistem melakukan pelacakan dan pemberian informasi yang tepat tentang diagnosa penyakit gigi dengan tingkat nilai akurasi sebesar 70%. berdasarkan perbandingan data dan hasil kesimpulan sistem.

Identifikasi Penulis Berdasarkan Pola Tulisan Tangan Menggunakan Convolutional Autoencoder dan KNN Muhammad Turmudzi; Endang Setyati
Journal of Electrical Engineering and Computer (JEECOM) Vol 3, No 1 (2021)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v3i1.1548

Identifikasi tulisan tangan dilakukan dengan beberapa tahapan, yaitu Akuisisi Citra dengan memanfaatkan mesin scanner dengan kualitas gambar 300dpi, Segmentasi dilakukan dengan metode threshold dan seleksi kontour dari gambar, penggabungan gambar hasil segmentasi, proses citra dari hasil segmentasi ke dalam Convolutional Autoencoder yang hasilnya diteruskan ke Transfer Learning (Lazy Learning) dalam hal ini penulis menggunakan metode KNN untuk mencocokkan tulisan tangan dari penulis. Penelitian dilakukan dengan menggunakan 100 dataset dari 20 penulis yang masing-masing penulis menulis 5 kali. Dataset yang digunakan di ujicoba pertama mengguanakan penggalan kalimat pada tulisan tangan yaitu Judul dari Puisi Chairil Anwar. Ujicoba dilakukan dengan membandingkan Training menggunakan Convolutional Autoencoder dan tanpa menggunakan Convolutional Autoencoder. Hasil dari ujicoba dengan Convolutional Autoencoder memperoleh nilai akurasi sebesar 89% dan tanpa menggunakan Convolutional Autoencoder, didapatkan nilai akurasi sebesar 88%. Pada ujicoba menggunakan tulisan tangan full, diperoleh hasil akurasi rata-rata 50% jauh di bawah hipotesa sehingga tidak cocok untuk diterapkan pada identifikasi tulisan tangan. Perlu ada nya pembatasan tulisan tangan yang akan digunakan sebagai dataset dalam identifikasi tulisan tangan

Co-Authors Abdur Rouf Achmad Firman Choiri Agung Adi Saptomo Agung Dewa Bagus Soetiono Ajeng Restu Kusumastuti Akhmad Solikin Andi Sanjaya Andi Sanjaya Andriyanto, Pyepit Rinekso Anggay Luri Pramana Arif Priyambodo Azis Suroni Bambang Widodo Devi Dwi Purwanto Dicka Y Kardono Edwin Pramana Eko Mulyanto Yuniarno Elis Fitrianingsih Elkana Lewi Santoso Esther Irawati Setiawan Fachrul Kurniawan Farkhan, Muhammad Febriantoro, Erfan Fery Satria Kristianto Fitrianingsih, Elis Francisca H Chandra Francisca Haryanti Chandra Gunawan Gunawan Gunawan Hans Keven Budi Prakoso Hatem Alsadeg Ali Salim Hendrawan Armanto Herman Budianto Honoris Setiahadi Ine Juniwati Joan Santoso Kartika, Bara Alpa Yoga Kholilul Rohman Kurniawan Lilis Setyaningsih Luhfita Tirta Lukman Zaman Luqman Zaman M. Najamudin Ridha Masrur Anwar Mauridhi Hery Purnomo Maysas Yafi' Urrochman Miftah Farid Mochamad Hariadi Muhammad Farkhan Muhammad Turmudzi Nanang Wahyudi Novi Duwi Setyorini Peter Winardi Pranama, Edwin Rafliana Natalia da Silva Raymond Sugiarto Raymond Sutjiadi Reddy Alexandro Harianto Resmana Lim Rizal Budi Rusina Widha Febriana Salim, Shierly Kartika San, Joan Soetiono, Agung Dewa Bagus Subroto Prasetya Hudiono Suharyono Az Suhatati Tjandra Supandik, Ujang Joko Surya Sumpeno Suyuti, Mahmud Tjwanda Putera Gunawan Tri Septianto Tuesday saka gustaf Udkhiati Mawaddah Uliontang Uliontang Yasir Arafat, Yasir Yosi Kristian Yuliana Melita Pranoto Yunita, Helda Zaman, Luqman

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