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Telematika : Jurnal Informatika dan Teknologi Informasi
Published by Universitas Pembangunan Nasional Veteran Yogyakarta
ISSN : 1829667X     EISSN : 24609021     DOI : 10.31315
Core Subject : Engineering,
Electrical & Electronics Engineering
Arjuna Subject : -
Articles 361 Documents
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Implementation of Histogram Equalization for Image Enhancement in The Classification of Spices Using K-Nearest Neighbor Safrizal, Busroni Ahmad; Kaswidjanti, Wilis
Telematika Vol 21 No 3 (2024): Edisi Oktober 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i3.12070

Abstract

Purpose: To determine the effect of implementing Histogram equalization (HE) at the image preprocessing stage to improve image quality in rhizome spice classification using the K-Nearest Neighbor classification method.Design/Method/Approach: Rhizome spice data was taken directly using a camera with a total of 600 images divided by a ratio of 80:20 for training and testing data. Preprocessing is done starting from resize to 512x512 pixels, then remove background to remove background objects that are not needed, then histogram equalization and also grayscale conversion. Glcm texture feature modeling, rgb color feature and hsv color feature are used as classification parameters. Classification is done using the K-Nearest Neighbor (KNN) method.Findings/result: The test results of this study can be concluded that the application of HE at the image preprocessing stage succeeded in improving classification performance as seen from the accuracy evaluation value. In KNN classification without preprocessing histogram equalization gets an accuracy of 73.8%.  When implementing histogram equalization the classification accuracy increases to 76.1%.From the two accuracy results obtained, it can be seen that the implementation of histogram equalization has a good effect in increasing the accuracy of classification.Originality/value/state of the art: The application of Histogram equalization (HE) in image preprocessing is able to improve image quality so that classification accuracy can increase compared to without using histogram equalization preprocessing.
Application of the Technological Acceptance Model(TAM) Approach to the Influence of Public Perceptions Using Digital Wallets Saputra, Elin Panca; Saputro, Achmat Yulyadi; Priyono, Priyono; Kusumo, Aryo Tunjung; Rahman, Taufik
Telematika Vol 21 No 1 (2024): Edisi Pertama 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.12083

Abstract

Purpose: This research aims to conduct a study of the perceptions of digital wallet users and how users' reactions influence the benefits of digital wallets in the big city of JakartaDesign/methodology/approach: The research method applied is a quantitative method. The population of this research is digital wallet users in the city of Jakarta. The number of samples applied was 121 respondents according to the purposeful sampling method. The data testing methods applied are convergent validity, discriminant validity, composite reliability and Cronbanch alpha. Data calculations apply Smart PLS 3 software. The results of this study show that trust and perceived risk do not influence user preferences for using digital walletsFindings/result: The results of this research constantly support a number of previous studies related to TAM where perceived usefulness and perceived ease of use play a direct and indirect role in interest in using digital wallets. So the community's perceived usefulness is a variable that has a prominent influence on the preferences of digital wallet users in the city of Jakarta.Originality/value/state of the art: The steps taken from the start of the study to its conclusion were designed to use the TAM approach to determine how the public felt about this particular study. This study uses quantitative research methods and yields two models: an inner model, or structural model, that includes path analysis through Smart PLS 3 data analysis, and an outer model, or measurement model, that includes composite reliability, conbranch alpha, discriminant validity, and convergent validity. 
Tahsin Tracker as an Effort to Improve the Services of the Islamic Studies Development Institute for Lecturers and Education Personnel at Ahmad Dahlan University Tarmuji, Ali; Masduki, Yusron
Telematika Vol 21 No 1 (2024): Edisi Pertama 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.12120

Abstract

Purpose: This research aims to produce a Tahsin Tracker application as a tool to overcome several problems in managing tahsin guidance organized by the Islamic Studies and Development Institute (LPSI) UAD for lecturers and education staff.Design/method/approach: The method used in system development by applying general software development methods. The stages adopted include data collection, needs analysis (user and system), design (process, database and interface), implementation (database and program, and system testing.Results: A Mobile Web-based Tahsin Tracker application has been produced and has been tested on potential users using black box and SUS testing. Black box testing is 100% valid. The SUS test obtained a score of 79. Based on the SUS score of 79, the application is in the Acceptance Range: ACCEPTABLE, Grade Scale: C, and Adjective Rating: EXCELLENT category.Originality/state of the art: Based on previous research, as well as the results of the development of existing tahsin applications, this research adopts and complements existing deficiencies and produces new assessments of the features and usability in related units, because it replaces the manual method of book record basis, and becomes the main alternative for developing institutional service media in a better direction.  
Klasifikasi Suara Berdasarkan Range Frekuensi Menggunakan Metode Fast Fourier Transform Untuk Mengetahui Jenis Suara Manusia Fitria, Liza; Novianda; Alfina, Devi
Telematika Vol 21 No 3 (2024): Edisi Oktober 2024
Publisher : Jurusan Informatika

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

Abstract

Dalam penelitian ini dirancang sebuah sistem pengelompokan jenis suara seseorang berdasarkan rentang frekuensinya yang berdasar pada rentang frekuensi dominan tuts piano sebagai acuannya. Suara pada setiap manusia memiliki tipe yang berbeda-beda. Terdapat 7 tipe suara manusia yaitu, alto, mezzo-sopran, dan sopran untuk perempuan. Bass, bariton, countertenor, dan tenor untuk laki-laki. Sistem ini diperuntukkan untuk mengelompokkan setiap suara yang berbeda-beda baik laki-laki maupun perempuan agar dapat mengetahui jenis suara setiap individu karena setiap suara atau bunyi yang dikeluarkan pasti memiliki sebuah nada dan jenisnya masing-masing. Permasalahan ini muncul dikarenakan kualitas dan jenis suara setiap orang pasti berbeda-beda tidak lagi hanya berdasarkan tinggi rendah nya saja, melainkan karena setiap suara belum tentu mempunyai jenis suara tetapi setiap suara pasti memiliki frekuensi dari range vokal yang kemudian menghasilkan nada, lalu jika nada ini dipadukan dengan cara yang tepat, maka akan tercipta harmonisasi yang indah. Sistem yang dirancang terdiri dari masukan data awal berupa suara yang bereksistensi dengan format WAV dengan ukuran file tidak lebih dari 200 Mb, kemudian di ekstraksi menggunakan FFT (Fast Fourier Transform) pada software Python dan data suara akan di olah dari domain waktu ke domain frekuensi sehingga menghasilkan grafik dan jenis suaranya. Fast Fourier Transform (FFT) adalah suatu algoritma untuk menghitung transformasi Fourier diskrit (Discrete Fourier Transform, DFT) dengan cepat dan efisien. Transformasi Fourier cepat diterapkan dalam berbagai bidang, mulai dari pengolahan sinyal digital, memecahkan persamaan diferensial parsial, dan untuk algoritma untuk mengalikan bilangan bulat besar. Dalam penelitian ini sistem dapat mengklasifikasikan suara berdasarkan range frekuensi yang sama atau dominan dengan range vokalnya sehingga menghasilkan frekuensi suara, jenis dan nada suara serta grafiknya. Penelitian ini menghasilkan nada tertinggi oleh Titin dengan frekuensi sebesar 1374.523 Hz dan suara terendah dapat diperoleh oleh Ulfa dengan frekuensi 71.242 Hz.
Implementation of Mel-Frequency Cepstral Coefficient As Feature Extraction Method On Speech Audio Data Marbun, Andre Julio; Heriyanto; Kodong, Frans Richard
Telematika Vol 21 No 3 (2024): Edisi Oktober 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i3.12339

Abstract

Sounds cannot be directly processed by machines without a feature extraction process being carried out first. Currently, there are so many choices of feature extraction methods that can be used, so determining the right feature extraction method is not easy. One method of feature extraction on sound signals that is often used is Mel-Frequency Cepstral Coefficient (MFCC). MFCC has a working principle that resembles the human hearing system, which causes it to be widely used in various tasks related to recognition based on sound signals. This research will use the MFCC method to extract characteristics on voice signals and Support Vector Machine as a method of emotion classification on the RAVDESS dataset. MFCC consists of several stages, namely Pre-emphasize, Frame Blocking, Windowing, Fast Fourier Transform, Mel-Scaled Filterbank, Discrete Cosine Transform, and Cepstral Liftering. The type of test design that will be carried out in this research is parameter tuning. Parameter tuning is carried out with the aim of obtaining parameters that produce the best accuracy in the machine learning model. The parameters that will be tuned include the α value in the Pre-Emphasis process, frame length and overlap length in the Frame Blocking process, the number of mel filters in the Mel-Scaled Filterbank process, the number of cepstral coefficients in the Discrete Cosine Transform process and the C value in SVM. The best accuracy in males of 85.71% was obtained with a combination of filter parameter pre-emphasize of 0.95, frame length of 0.023 ms, overlap of adjacent frames of 40%, number of mel filters in the mel-scaled filterbank process of 24 mel, number of cepstral coefficient of 24 coefficient and the value of 'C' in SVM of 0.01. The best accuracy in women of 92.21% was obtained with a combination of filter parameters pre-emphasize of 0.95, frame length of 0.023 ms, overlap of adjacent frames of 40%, the number of mel filters in the melscaled filterbank process of 24 mel, and the number of cepstral coefficient of 13 coefficient and 'C' value in SVM of 0.01. From the two test results of tuning parameters between men and women, there are similar parameter values in all test parameters, except for the number of cepstral coefficients. The number of cepstral coefficient in men is 24 coefficient while the number of cepstral coefficient in women is 13 coefficient. Based on the research conducted, there are the following conclusions, the combination of MFCC and SVM methods can be used for emotion classification based on input data in the form of voice intonation with an accuracy of 85.71% in men and 92.21% in women. The difference in accuracy obtained between male and female models is due to the different data used. Male models are trained with male voice data and female models are trained with female voice data, this is done because men and women have different voice frequency ranges.
Performance Analysis of FastAPI Framework on Lost Circulation Handling Management Application in Oil Well Drilling Suryotomo, Andiko Putro; Akbar, Bagus Muhammad; Husaini, Rochmat
Telematika Vol 21 No 1 (2024): Edisi Pertama 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.13259

Abstract

Purpose: This study aims to conduct a load testing using JMeter and then analyze the performance of the FastAPI framework on the backend of the lost circulation handling management application in oil well drilling. The developed API receives input in the form of drilling parameter data (daily drilling report) from drilling engineers to be processed by a machine learning model (prediction and classification) through the FastAPI framework. The developed API returns processing data in JSON format.Methodology: Performance measurement is done by conducting load testing simulations using the help of JMeter software. Load testing scenarios are created by varying the number of users and ramp-up time, as well as the method of loading the machine learning model used (normal or preemptive loading). The parameters measured in the test scenario are average execution time, maximum execution time, error percentage, and request throughput.Findings: Load testing on a FastAPI-developed API demonstrated that for compute-heavy tasks like machine learning inference, increasing the number of processor cores and using preemptive model loading led to significantly better performance improvements than changes in processor clock speed or switching from HDD to SSD. Even when simulating a higher user load than initially expected (up to 250 users/threads), FastAPI maintained good response times and a low error rate, remaining below 20%.Originality/value/state of the art: This study result is an information about the performance of the FastAPI framework in the application of lost circulation handling management in oil well drilling in the deployment phase, not only up to the model testing phase as in previous studies. 
Maturity Level Analysis of Furniture MSME Business Processes in Yogyakarta Nour, Azty Acbarrifha; Hananto, Mursid Wahyu
Telematika Vol 21 No 3 (2024): Edisi Oktober 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i3.13409

Abstract

AbstrakTujuan: Penelitian ini bertujuan untuk mendeskripsikan tingkat kematangan proses bisnis UMKM Furnitur di Yogyakarta berdasarkan pengukuran BPOMM.Desain/metodologi/pendekatan: Penelitian ini mengumpulkan data melalui observasi dan wawancara dengan pemilik UMKM mebel di Kota Yogyakarta. Hasil pengumpulan data kemudian dianalisis menjadi elemen pengukuran BPOMM.Temuan/Hasil: Tingkat kematangan proses bisnis UMKM Furnitur di Yogyakarta dan saran perbaikan proses bisnis tersebut.Orisinalitas/nilai/keadaan terkini: Penelitian ini menunjukkan tingkat kematangan proses bisnis UMKM Furnitur di Kota Yogyakarta berdasarkan pengukuran BPOMM beserta saran perbaikan proses bisnisnya.
Rubber Leaf Image Classification Using Artificial Intelligence Methods as an Effort to Improve Plantation Production Results Buyung, Irawadi; Utari, Evrita Lusiana; Mustiadi, Ikhwan; Winardi, Sugeng; Ariyanto, Ipan; Listyalina, Latifah
Telematika Vol 21 No 3 (2024): Edisi Oktober 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13587

Abstract

Purpose: Rubber is one of the plantation commodities that contributes positively to the trade surplus in the agricultural sector. Seeing the positive trend in global rubber consumption and production, demand is expected to continue increasing in the future. To enhance rubber productivity, rubber processing technology can be used to make it more efficient, thus increasing the amount of latex extracted from the sap and reducing waste materialDesign/methodology/approach: One technology that can be developed to increase the productivity efficiency of rubber plants is by using Artificial Intelligence. This technology is expected to be implemented in the rubber plantation sector, specifically in the automatic recognition of rubber leaves.Findings/result: The measurement and performance analysis of the rubber leaf image classification algorithm based on Artificial Intelligence has also been evaluated, showing near-perfect accuracy on training data (99.86%) and very good performance on validation data (97.43%), with a very low validation loss (0.0873), indicating that the model has learned well by the last epochOriginality/value/state of the art: The population in this study consists of image data from various tree leaves, including 10 types of rubber leaves and non-rubber leaves 
Analisis Suhu Permukaan Lahan dan Vegetasi untuk mengukur Urban Heat Island menggunakan Google Earth Engine. M.T, Munsyi
Telematika Vol 21 No 3 (2024): Edisi Oktober 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i3.13810

Abstract

Banjarmasin, yang sering disebut sebagai kota seribu sungai, telah mengalami perkembangan perkotaan yang signifikan, yang mengarah pada intensifikasi efek Urban Heat Island (UHI). Studi ini menggunakan kuantitatif dengan analisis berbasis spasial memanfaatkan data satelit Landsat 8, dikombinasikan dengan kemampuan Google Earth Engine, untuk mengkaji interaksi antara Suhu Permukaan Lahan (LST) dan Indeks Vegetasi Ternormalisasi (NDVI) dari tahun 2013 hingga 2023. Pengamatan awal menunjukkan pola LST yang bervariasi, terutama di area sekitar sungai, serta variasi NDVI yang signifikan yang mencerminkan kondisi vegetasi. Analisis mendetail menunjukkan adanya hubungan yang jelas antara tutupan vegetasi dan suhu perkotaan, dengan fluktuasi suhu yang berkorelasi dengan perubahan vegetasi dan aktivitas antropogenik. Khususnya, pandemi COVID-19 global berperan dalam penurunan LST selama 2020-2021, meskipun cakupan vegetasi tetap relatif stabil. Temuan ini menekankan pentingnya perencanaan kota berkelanjutan, dengan penekanan pada pelestarian dan integrasi ruang hijau untuk mengurangi efek UHI dan mempromosikan keseimbangan lingkungan. Penelitian ini memberikan wawasan yang berharga tentang dinamika iklim perkotaan, kesehatan vegetasi, dan strategi pembangunan berkelanjutan untuk kota-kota yang mengalami urbanisasi cepat.
Optimization of stock price prediction of PT Aneka Tambang Tbk (ANTM) using Long Short-Term Memory Maulana, Ilham; Mellisa, Mellisa; Jumanto, Jumanto
Telematika Vol 22 No 1 (2025): Edisi Februari 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i1.10921

Abstract

Purpose: Develop a machine learning model to predict stock market activity by finding the Root Mean Squared Error (RMSE) value.Design/methodology/approach: LSTM (Long Short-Term Memory) is one of the machine learning techniques that can be used to anticipate traffic in realtime. Using this method can be used to analyze stock market activity that has time series data.Findings/result: This research obtained a Root Mean Squared Error (RMSE) value of 43.32.Originality/value/state of the art: By using the same machine learning method as the previous research, namely LSTM. The research provides more efficient results.

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