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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 Register: Jurnal Ilmiah Teknologi Sistem Informasi 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 JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) 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 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
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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Sistem Rekomendasi Pekerjaan Menggunakan Content Based Similarity Abdur Rouf; Yuliana Melita Pranoto; Endang Setyati
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 12, No 2: Agustus 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v12i2.1229

Abstract

Based on data from the Central Statistics Agency (BPS) it is stated that the data for people who did not have a job from August 2019 to August 2021 recorded an increase from 7104.42 to 9102.05, meaning that within 2 years people who did not have a job had increased significantly. This is caused by one of the factors, namely finding information on job vacancies which is difficult, users still have to choose one job at a time in accordance with their field of knowledge. By building a job recommendation system, users will find it easier to find suitable job information, the data used is obtained from 1120 (one thousand one hundred and twenty) alumni data which includes academic grades, non-academic scores, positions and companies obtained from alumni data from the Institute of Technology and Business Widya Gama Lumajang. Using a content-based similarity algorithm with machine learning techniques using the MLP classifier feature and several trials using different parameters in each experiment. In each experiment the researcher used 10 (ten) samples. The results of this trial the machine learning feature of the MLP classifier can be concluded to be able to provide an accuracy of 81% with a precision value of 0.77, a recall of 0.81 and an f1-score of 0.76.The results of this study are used by users or fresh graduates to get job recommendations in accordance with their field of study.Keywords: Content Based Similarity; Interaciton Based Relation; Job Recommendation System AbstrakBerdasarkan data Badan Pusat Statistik (BPS) menyebutkan bahwa data orang yang tidak memiliki pekerjaan dari agustus 2019 sampai dengan agustus 2021 tercatat naik dari angka 7.104,42 menjadi 9.102,05 artinya dalam kurun 2 tahun orang yang tidak memiliki pekerjaan mengalami kenaikan secara signifikan. Hal ini disebabkan oleh salah satu faktor yaitu mencari informasi lowongan pekerjaan yang sulit pengguna masih harus memilih satu per satu pekerjaan yang sesuai dengan bidang ilmunya. Dengan membangun sistem rekomendasi pekerjaan pengguna akan lebih mudah menemukan informasi pekerjaan yang sesuai, data yang digunakan diperoleh dari data alumni sebanyak 1.120 (seribu seratus dua puluh) yang mencakupi nilai akademik, nilai non akademik, jabatan dan perusahaan yang diperoleh dari data alumni Institut Teknologi Dan Bisnis Widya Gama Lumajang. Menggunakan algoritma content-based similarity dengan teknik machine learning fitur MLP classifier dan beberapa kali uji coba menggunakan parameter yang berbeda-beda pada setiap percobaannya. Pada setiap percobaan peneliti memakai 10 (sepuluh) sample. Hasil dari uji coba ini machine learning fitur MLP classifier dapat disimpulkan mampu memberikan akurasi sebesar 81% dengan nilai precision 0.77 recall 0.81 dan f1-score 0.76. Hasil penelitian ini digunakan oleh pengguna atau fresh graduate untuk mendapatkan rekomendasi pekerjaan yang sesuai dengan bidang ilmu. 
Prediksi Timing Financial Distress Pada Bank Perkreditan Rakyat di Indonesia Menggunakan Machine Learning Maysas Yafi' Urrochman; Endang Setyati; Yosi Kristian
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 12, No 2: Agustus 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v12i2.1219

Abstract

There is no system that can provide early warning of financial problems that threaten the operations of Rural Banks (BPR), so it is necessary to predict the timing of financial distress in BPRs in Indonesia using a two-stage classification and regression technique. Researchers used BPR financial report data in Indonesia for 4 years as a research sample, with a total of 150 Financial Ratio Data, consisting of 50 bankrupt financial ratio data and 100 non-bankrupt BPRs. Data analysis was carried out 2 years before being declared bankrupt. The target classification is divided into 5 classes: very healthy, healthy, moderately healthy, less healthy, distressed. The results of the study concluded: a two-stage classification and regression technique can be used to predict the timing of financial distress. This is evidenced by the results of the MLP Classifier classification with an accuracy rate of f1-score of 87%. The results of the evaluation of timing predictions using Random Forest Regression showed a mean absolute error of 1.8 months and a mean absolute percentage error of 4%.Keywords: Rural Banks; Financial Distress; Random Forest Regression; Support Vector MachineAbstrakBelum ada suatu sistem yang dapat memberikan peringatan dini adanya permasalahan keuangan yang mengancam operasional Bank Perkreditan Rakyat (BPR), sehingga perlu memprediksi timing financial distress pada BPR di Indonesia menggunakan teknik dua tahap klasifikasi dan regresi. Peneliti menggunakan data laporan keuangan BPR di Indonesia selama 4 tahun sebagai sampel penelitian, dengan jumlah data 150 Data Rasio Keuangan, terdiri dari 50 Data rasio keuangan Pailit dan 100 BPR tidak pailit. Analisis Data dilakukan 2 tahun sebelum dinyatakan Pailit. Target klasifikasi dibagi menjadi 5 kelas: sangat sehat, sehat, cukup sehat, kurang sehat, distress. Hasil penelitian menyimpulkan: teknik dua tahap klasifikasi dan regresi dapat digunakan untuk memprediksi timing financial distress. Ini dibuktikan dengan hasil klasifikasi MLP Classifier dengan tingkat akurasi f1-score sebesar 87%. Hasil evaluasi prediksi timing menggunakan Random Forest Regression menunjukkan hasil mean absolute error sebesar 1,8 bulan dan hasil mean absolute percentage error sebesar 4%. 
Detection of Lung Cancer Malignancy Types on CT-Scan Using the Convolutional Neural Network Method at PHC Hospital Surabaya Kholilul Rohman Kurniawan; Endang Setyati; Francisca Haryanti Chandra
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 1 (2024): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i1.6

Abstract

There are many uses for digital image processing, ranging from tumor and cancer detection in the body to reading blood cells. The rate of lung cancer represents about 13.27% of the total cancer cases, and this shows that lung cancer is the main type of disease in men. Lung cancer is one of the most dangerous and life-threatening diseases in the world. In Indonesia, lung cancer is more often detected when patients are at an advanced stage. Therefore, in this paper, we applied Deep Learning to solve a lung cancer malignant detection system; it is used to detect and classify nodule areas. So that lung cancer detection can be obtained with accurate results. This paper explains the working system for detecting lung cancer malignancies using a Convolutional Neural Network (CNN) and the model architecture for training the dataset using the EfficientNet model. This study collected 800 lung CT images from PHC Surabaya Hospital in DICOM format. A total of 13 layers with EfficientNet architecture and classification layers for each type of cancer class have been used in the model. The experimental results of the model achieved satisfactory results with an accuracy of 99.46%, with a maximum epoch of 30 and a mini-batch size of 128.
PEMODELAN PREDIKSI KUANTITAS PENJUALAN MAINAN MENGGUNAKAN LightGBM Febriantoro, Erfan; Setyati, Endang; Santoso, Joan
SMARTICS Journal Vol 9 No 1 (2023): SMARTICS Journal (April 2023)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v9i1.8279

Abstract

The main characteristic of the toy industry is its rapid change and uncertainty. Demand, influenced by certain trends, can change abruptly and suddenly disappear when the next viral product takes over the market. Constant product innovation, short life cycles and high cannibalization rates have the potential to incur higher relative costs compared to other industries in terms of inventory obsolescence, lost sales and reduced prices. Based on these problems, a study was conducted to predict toy sales using the LightGBM algorithm model in a time-series form with a sales dataset of 460 toy items classified into 14 categories within a time span of 1,353 days with a prediction period of 1, 3, and 6 months. This study produced 42 models based on product category and prediction period, with the best RMSE value of 0.0042 in the KARTU toy model, and 3 models for all categories based on the prediction period with the best RMSE value of 0.0380 in the 1 month prediction period.
Hand Gesture Recognition Sebagai Pengganti Mouse Komputer Menggunakan Kamera Yunita, Helda; Setyati, Endang
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 3 No. 2 (2019)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v3i2.114

Abstract

Akhir-akhir ini perkembangan teknologi semakin pesat, metode interaksi dan komunikasi antara pengguna dengan komputer adalah salah satu tuntutan perkembangan teknologi. Berbagai macam pembaharuan teknologi mengusahakan untuk meminimalisir berbagai macam perangkat menjadi satu agar lebih mudah digunakan. User lebih membutuhkan peralatan komunikasi yang bersifat alami karena tidak membutuhkan kontak langsung dengan peralatan input. Misalnya dengan gerakan dari tubuh manusia didepan kamera komputer sudah bisa menginterpretasikan. Untuk mengatasi masalah tersebut maka dilakukan suatu penelitian tentang deteksi isyarat tangan. Inputan berupa isyarat dan gerakan tangan didepan kamera dapat memberikan aksi pergerakan pada mouse yang diistilahkan dengan kamera mouse. Metode yang digunakan adalah convexhull algorithm. Melalui convexhull algorithm bisa didapatkan jumlah jari tangan yang kemudian dapat dijadikan acuan dalam pengerjaan aksi mouse. Sebenarnya sudah banyak penelitian tentang camera mouse, tetapi implementasinya masih banyak yang bergantung dengan peralatan tambahan. Penelitian ini mengembangkan penelitian yang sudah ada, yaitu hand gesture recognition dengan implemen-tasi pergerakan mouse dari video secara realtime. Dengan hand gesture recognition dan menggunakan metode convexhull algorithm pengenalan tangan akan lebih mudah hanya dengan menggunakan kamera, hanya dengan hitungan detik aksi mouse pada komputer dapat berjalan dengan baik yaitu dengan tingkat akurasi sebesar 68 % dari 75 kali percobaan
Pengukuran Nilai Keseimbangan Gerakan Manusia terhadap Dataset Tari Remo dengan High-level Matrix/Array Language Salim, Shierly Kartika; Zaman, Lukman; Setyati, Endang
J-INTECH (Journal of Information and Technology) Vol 11 No 2 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i2.829

Abstract

Remo Dance is one of Indonesia’s traditional dance that originated from East Java and need to be preserved. The preservation act from collaboration of the Goventment and the Society started with a mass Remo Dance at Surabaya city on the year of 2022 that successfully granted a MURI record. This research is meant to support all the effort by calculating balance of the movements as one of the aspect of biomechanics. Biomechanics it self is a branch of study that learns about movement mechanism of living organism. The calculation of balance as an ability to keep a posistion on the change of movements is aim to analyze which movements has the most difficulty in balance and which one is the most stable from the chunk of motion capture data. Computation is done with high-level matrix/array language and the data form is a biovision hierarchy file (BVH). The Visualization of data shows that ‘ucek-ucek’ motion is the most stable movements, while 360 degree spin motion is a difficult movements and require great balance.
Identifikasi Viseme Untuk Fonem Bahasa Madura Berbasis Clustering Berdasarkan Facial Landmark Point Andriyanto, Pyepit Rinekso; San, Joan; Setyati, Endang
J-INTECH (Journal of Information and Technology) Vol 11 No 1 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i1.835

Abstract

The most effective form of language in communicating is spoken or spoken language. When speaking humans will move their mouth and lips to say certain words. This mouth and lip movement model describes a viseme (visual-phonem), namely a group of phonemes that have a visual or almost the same appearance. Madurese language is a unique language and has certain characteristics. In addition to having a language level, Madurese has aspirated phonemes or exhaled word pronunciations such as: /bh/, /dh/, /Dh/, /gh/ and /jh/ which do not exist in other languages. This research discusses the identification of viseme classes for Madurese phonemes based on clustering based on facial landmark points. Of the 47 Madurese language phonemes, 9 Madurese language visemes were obtained from the K-Means clustering process. The clustering process uses feature extraction based on facial landmark points so that the distance calculation for each feature is obtained. The features used are geometric features. The Madurese viseme model is used to build 2D mouth animations in uttering Madurese words or sentences based on input in the form of text. The benefit of this research is for learning purposes in pronouncing Madurese words or sentences, because Madurese has different writing and pronunciation.
Factors Influencing Users in Using Mobile Banking with the Extended Unified Technology Acceptance and Use of Technology Model Supandik, Ujang Joko; Pranama, Edwin; Setyati, Endang
EduTech Journal Vol. 2 No. 1 (2025): JET-MAY
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/zyrenc18

Abstract

As one of the most innovative and new technologies, Mobile banking is a good example of the breakthrough of mobile technology in the banking sector, allowing customers to independently perform financial transactions (i.e. balance inquiries, fund transfers, bill payments) via mobile devices, smartphones, or Personal Digital Assistants (PDAs) at a time and place chosen by the customer. Customers in Indonesia have now widely used the internet for financial services. This study aims to analyze the factors that influence users in using mobile banking in Indonesia using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. This study identifies key variables such as security, trust, performance expectations, social influence and user intentions in adopting mobile banking in Indonesia. The method used in this research is to conduct a survey by distributing questionnaires using Google Form and distributed via WhatsApp, Instagram and word of mouth, at least 400 respondents to mobile banking users. The results of the analysis show that all factors have a significant influence on user intention to use mobile banking. In addition, the Trust and security variables play a very important role in adopting mobile banking. These findings are expected to provide solutions for mobile banking providers in.
Recognizing the Types of Beans Using Artificial Intelligence Nafi'iyah, Nur; Setyati, Endang; Kristian, Yosi; Wardhani, Retno
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 2 (2023): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i2.3054

Abstract

Many studies have previously addressed the recognition of plant leaf types. The process of identifying these leaf types involves a crucial feature extraction stage. Image feature extraction is pivotal for distinguishing the types of objects, thus demanding optimal feature analysis for accurate leaf type determination. Prior research, which employed the CNN method, faced challenges in effectively distinguishing between long bean and green bean leaves when identifying bean leaves. Therefore, there is a need to conduct optimal feature analysis to correctly classify bean leaves. In our research, we analyzed 69 features and explored their correlations within various image types, including RGB, L*a*b, HSV, grayscale, and binary images. The primary objective of this study is to pinpoint the features most strongly correlated with the recognition of bean leaf types, specifically green bean, soybeans, long beans, and peanuts. Our dataset, sourced from farmers' fields and verified by experienced senior farmers, consists of 456 images. The most highly correlated feature within the bean leaf image category is STD b in the L*a*b image. Furthermore, the most effective method for leaf type recognition is Neural Network Backpropagation, achieving an accuracy rate of 82.28% when applied to HSV images.
Augmented Reality Marker Based Tracking Visualisasi Drawing 2D ke dalam Bentuk 3D dengan Metode FAST Corner Detection Wahyudi, Nanang; Harianto, Reddy Alexandro; Setyati, Endang
Intelligent System and Computation Vol 1 No 1 (2019): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v1i1.28

Abstract

Augmented Reality (AR) memungkinkan pengguna dapat melihat objek virtual pada lingkungan nyata. Berbeda dengan Virtual Reality (VR) yang membawa pengguna masuk pada lingkungan virtual sepenuhnya tanpa melihat dunia nyata. Teknologi AR menggunakan marker sebagai target untuk memunculkan objek virtual. Penelitian ini menggunakan Drawing 2D sebagai Marker Based Tracking dalam mendeteksi target untuk memunculkan objek 3D virtual. Gambar 2D atau Drawing 2D merupakan alat untuk menyampaikan maksud dan informasi dari drafter kepada teknisi. Lulusan siswa Sekolah Menengah Kejuruan (SMK) harus mampu memahami Drawing 2D dan memvisualisasikan kedalam bentuk 3D. Kemampuan spasial dalam memvisualisasi ini yang harus dimiliki karena menyangkut masa depan setiap siswa. Pembelajaran materi proyeksi Drawing 2D memerlukan teknis khusus agar mampu di pahami oleh siswa. Aplikasi AR ini menggunakan metode Features from Accelerated Segment Test Corner Detection (FCD) dalam proses tracking. Uji coba penelitian menggunakan 50 marker. Kriteria uji coba deteksi marker dengan posisi tegak lurus, miring 30°, 45°, 60° dan 75° terhadap kamera serta dengan jarak deteksi 20 cm, 30 cm,40 cm,50 cm,dan 60 cm. Dari hasil uji coba untuk deteksi 50 marker disimpulkan bahwa marker dapat terdeteksi pada jarak 50 cm dengan posisi marker tegak lurus, kemiringan 30° maksimum pada jarak 40 cm, dan kemiringan 45° maksimum pada jarak 30 cm. Proses deteksi marker dipengaruhi oleh tingginya spesifikasi perangkat yang digunakan dalam ujicoba, pencahayaan serta besarnya marker yang digunakan.
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 Budi, Rizal Devi Dwi Purwanto Dicka Y Kardono Edwin Pramana Eko Mulyanto Yuniarno Elis Fitrianingsih Esther Irawati Setiawan Fachrul Kurniawan Farkhan, Muhammad Febriantoro, Erfan Fery Satria Kristianto Fitrianingsih, Elis Francisca H Chandra Francisca Haryanti Chandra Gunawan Gunawan Gunawan Gunawan, Tjwanda Putera Hans Keven Budi Prakoso Harianto, Reddy Alexandro 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 Mochamad Hariadi Muhammad Farkhan Muhammad Turmudzi Nafi'iyah, Nur Novi Duwi Setyorini Peter Winardi Pranama, Edwin Raden Mohamad Herdian Bhakti Rafliana Natalia da Silva Raymond Sutjiadi Reddy Alexandro Harianto Resmana Lim Retno Wardhani Rusina Widha Febriana Salim, Shierly Kartika San, Joan Santoso, Elkana Lewi Soetiono, Agung Dewa Bagus Subroto Prasetya Hudiono Sugiarto, Raymond Suharyono Az Suhatati Tjandra Supandik, Ujang Joko Surya Sumpeno Suyuti, Mahmud Tjwanda Putera Gunawan Tri Septianto Tuesday saka gustaf Udkhiati Mawaddah Uliontang Uliontang Wahyudi, Nanang Yosi Kristian Yuliana Melita Pranoto Yulius Widi Nugroho Yunita, Helda Zaman, Luqman