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INDONESIA
JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Published by STMIK Global Informatika MDP
ISSN : 24074322     EISSN : 25032933     DOI : -
Core Subject : Science,
Computer Science & IT
JATISI bekerja sama dengan IndoCEISS dalam pengelolaannya. IndoCEISS merupakan wadah bagi para ilmuwan, praktisi, pendidik, dan penggemar dalam bidang komputer, elektronika, dan instrumentasi yang menaruh minat untuk memajukan bidang tersebut di Indonesia. JATISI diterbitkan 2 kali dalam setahun (September dan Maret), makalah yang diterbitkan JATISI minimal terdiri dari 60% dari luar Sumatera Selatan, dan 40% dari Sumatera Selatan. Makalah yang diterbitkan melalui tahap review oleh reviewer yang berpengalaman dan sudah memiliki makalah yang diterbitkan di jurnal internasional yang terindeks SCOPUS.
Arjuna Subject : -
Articles 1,216 Documents
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Klasifikasi Daun Herbal Berdasarkan Fitur Bentuk dan Tekstur Menggunakan KNN Meiriyama Meiriyama; Siska Devella; Sandra Mareza Adelfi
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 3 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i3.2974

Abstract

Indonesia has an abundance of biodiversity. From a total of 40,000 types of herbal plants known in the world, there are approximately 30,000 types of herbal plants in Indonesia. Herbal plants are plants that are commonly used by people, especially in Indonesia, which have biodiversity as ingredients for making herbal medicines. Herbal plants are certainly not easy to recognize even though they often grow around the environment. Because there is still a lack of community knowledge about herbal plants, it is not possible to use these herbal plants. This study aims to classify the leaves of herbal plants using the K-Nearest Neighbor (KNN) method with k value is 3 and feature extraction of Histogram of Oriented Gradient (HOG) and Local Binary Patterns (LBP). The research was conducted on 15 types of herbal plants. Accuracy HOG method with KNN is 92.67%, Accuracy LBP with KNN is 88.67% and accuracy combination of HOG and LBP features with KNN method is 92.67%. Based on the three experiment scenarios that have been carried out, it shows that the combination of HOG and LBP features does not affect the accuracy of leaf classification of herbal plants.
Analysis of EfficientNetV2 Model Usage in Predicting Gender on the Face of Mask Users Novendra Adisaputra Sinaga
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 3 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i3.2975

Abstract

A technique for identifying physical traits or human behavior that is utilized as input for pattern recognition is called biometrics. Each type of biometric identification undoubtedly employs a unique technology. In order to do research on how to promote or sell items in accordance with visitor gender, a gallery or exhibition, such as a movie theater, retail mall, or exposition, needs visitor information from the event. The EfficientNetV2 model, a New Family in the Covolution Neural Network (CNN) family, outperforms the previous model in terms of parameter efficiency and training speed. According to tests, the EfficientNetV2 model can learn up to 6.8.The results using the EfficientNetV2 model were carried out for 25 epochs and there were 2 classes, namely male and female, each of which consisted of 72,318 training data and 16,813 testing data. The accuracy value for training is 0.9455 (94.5%) and for data testing the accuracy value is 0.9475 (94.7%). The loss value for training is 0.1375 (13.75%) and for testing data the loss value is 0.1277 (12.7%).
Pemetaan Proses Bisnis pada Entrrepreneurial Universities Lenny Rosita
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 3 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i3.2992

Abstract

Business process mapping is no longer an option but a necessity for organizations that focus on sustainable growth. Efficient and effective business processes increase work productivity and potentially even reduce costs. This research maps business processes level 0 (function), level 1 (process), and level 2 (sub-process) with a value chain approach and BPMN (Business Process Modeling Notation) 2.0. This study aims to develop a comprehensive business process model by collecting qualitative data, which integrates the 9 (nine) criteria for university accreditation requirements, AUN-QA at institutional level, ISO 9001:2015, and ISO 21001:2018. Business process modeling is carried out through 3 stages namely data collection, analysis, and observation. Data was collected through Focus Group Discussions (FGD), literature studies, and observations which are then analyzed, classified, modeled, and validated horizontally and vertically. There are 14 primary activities and 11 supporting activities in universities, where teaching and learning, research, and community service are the core of the primary process. Business process modeling level 1 which is breakdown into level 2 and so on makes it easier to review the process so that an efficient and effective process model is obtained. The results of the mapping are also useful for preparing organizational structures and mapping KPIs (Key Performance Indicators) that support organizational achievements.
PENENTUAN KARYAWAN TERBAIK DENGAN MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING PADA PERUSAHAAN KONTRAKTOR Bella Jenni Ourelia; Fransiska Prihatini Sihotang
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 3 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i3.3037

Abstract

PT MPR is company that run in contractor industry. One of aspect that important is company human resource management employee performance assessment. This company use employee performance assessment, but when using it theres still some problem when calculating progress it take little bit time because theres a lot of employee in the company and the result of calculating not really accurate because of manual calculating. The purpose developing this decission support system in PT MPR is to helping HRD deciding who’s the best employee on company, so when calculating evaluation doesn’t take a lot of time and faster also accurate. Methodology developing this system using Rational Unified Process (RUP) that contain inception, elaboration, construction and transition. Methodology decision support using Simple Additive Weighting (SAW). Application made using website based and using visual studio code, programming language using PHP and MySQL. Thats why writer help solving problem with developing this decision support system for choosing the best employee with using SAW method
Penerapan Sistem Keamanan Video Menggunakan Kriptografi Algoritma Kunci Simetris Emi Suryadi; Moh Subli; Karina Nurwijayanti
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 3 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i3.3044

Abstract

Keamanan yang diterapkan pada video sebagai bentuk usaha dalam menjaga keaslian informasi yang disampaikan agar tetap terjaga dengan baik. Sistem keamanan yang digunakan dalam melindungi video dari serangan luar dengan menggunakan aplikasi kriptografi. Aplikasi kriptografi yang digunakan berhasil membuat video menjadi samar atau tidak jelas sehingga menyulitkan pihak yang tidak berwenang mengetahui informasi yang disampaikan proses ini disebut dengan enkripsi, sedangkan proses untuk melihat video agar dapat dibaca dengan mudah dikenal dengan dekripsi. Kunci yang digunakan untuk proses enkripsi dan juga dekripsi video menggunakan kunci simetris. Kunci simetris tetap harus dirahasiakan dengan tujuan untuk menjaga keaslian video sebab jika kunci yang digunakan diketahui oleh pihak yang tidak bertanggung jawab maka sistem keamanan video dapat dengan mudah dibaca. Tingkat keamanan video yang dienkripsi didasarkan kombinasi kunci yang digunakan, semakin banyak kombinasi yang digunakan maka tingkat kerumitan pemecahan sandi semakin kuat. Metode hill cipher menggunakan model matematika dalam mengacak suatu frame video sehingga membuat informasi tidak jelas. Perhitungan metode ini menggunakan model matriks dalam menghitung piksel frame video dengan modulo 256. Penerapan metode ini berhasil membuat video menjadi terenkripsi dan juga dapat mengembalikan video menjadi asli. Aplikasi kriptografi video ini dapat digunakan untuk mengurangi kejahatan penyalahgunaan informasi.
PENGGUNAAN KLASIFIKASI SAYUR SEGAR DAN SAYUR BUSUK MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Ery Hartati
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 7 No 3 (2020): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v7i3.3160

Abstract

Sayur merupakan salah satu makanan yang sering dikonsumsi oleh berbagai kalangan umur karena sumber berbagai mineral, vitamin dan serat pangan. Untuk memperoleh manfaat yang terdapat pada sayur, masyarakat harus mengonsumsi sayur yang segar dan belum busuk. Secara fisik, kesegaran sayur dapat dilihat karena tanda-tanda yang ada pada sayur segar atau sayur busuk mudah diamati.LBP (Local Binary Pattern) adalah metode ekstraksi fitur tekstur yang sederhana,namun efisien dalam mempresentasikan ciri tekstur, sedangkan HSV (Hue, Value dan Saturation) merupakan ruang warna yang cocok untuk mengidentifikasi warna-warna dasar yang akan digunakan dalam penelitian sebagai warna identifikasi cahaya dan bisa menoleransi perubahan intensitas cahaya. Penelitian ini menggunakan public dataset sayur segar dan sayur busuk. Proses di mulai dari resize menjadi ukuran 300 x 300 pixel dan selanjutnya dilakukan ekstraksi fitur LBP dan dilanjutkan dengan ekstraksi fitur HSV. Hasil ekstraksi fitur LBP dan HSV di gunakan sebagai input klasifikasi menggunakan algoritma Support Vector Machine dengan nilai n_estimator 500,1000,1500,dan 2000. Hasil pengujian menggunakan algoritma Support Vector Machine menghasilkan nilai Accuracy tertinggi sebesar 65,02% pada nilai γ =0,04
An Integrated Rice Quality Control Application in Central Java using the Feature-Driven Development (FDD) Method Diovianto Putra Rakhmadani; Gita Fadila Ftiriana; I Putu Restu Indriawan; Tsabitatul Iffah
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 3 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i3.3179

Abstract

Rice is the main food commodity in Indonesia. With a consumption level in 2021 of 31.9 million tons, an increase of 351.71 thousand tons compared to the previous year. However, this increase in the amount of production does not increase with an increase in rice quality. Based on BPS data in 2021, the import of quality rice is 41,800 tons. This is because there are still seekers of quality rice as the main food ingredient. To achieve rice self-sufficiency in Central Java Province and produce good quality rice, monitoring must be carried out starting from upstream (farmers) to downstream (distributors) of rice. The Integrated Rice Quality Control Application is designed to answer these problems. With the development of the Feature-Driven Development (FDD) software development method, the application can ensure the quality of rice can be maintained properly. The system design is carried out with an approach that aims to make it easier for its users. The design of this application is also proven to be acceptable for its users with the level of SUS testing with an average score of 70.95 which has been designed in the good category and can be implemented.
Analisis Kualitas Pengalaman Pengguna Sistem Pengelola Jurnal Menggunakan Metode User Experience Questionnaire Ahmad Farisi; Mardi Wicaksana
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 3 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i3.3328

Abstract

SIPENAMAS is a Research and Community Service System used by University MDP Lecturers to facilitate those who will submit and report research activities and community service. By using the UEQ method to measure the of the University MDP SIPENAMAS website. The purpose of the study was to determine and analyze the level of the University MDP SIPENAMAS website using the UEQ method. which consists of 6 variables, namely attractiveness, perpicuity, efficiency, dependability, stimulation, and novelty. The data collection process was carried out by distributing s to users of the University MDP SIPENAMAS Website, using a random sampling technique. In processing the data obtained using special software for the UEQ method. The results of the analysis with the UEQ are found that the value of the 6 scales, namely attractiveness 1.10, clarity 0.98, efficiency 0.88, accuracy 1.13, stimulation 0.92, and novelty 0.57 on the SIPENAMAS MDP website gets a score below average. Recommendations in this study are expected to provide consideration for improving on the SIPENAMAS MDP website.
Model Klasifikasi Berita Palsu Menggunakan Bidirectional LSTM dan Word2vec sebagai Vektorisasi Juanda Antonius Pakpahan; Yeni Chintya Panjaitan; Junita Amalia; Melani Basaria Pakpahan
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 4 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i4.1332

Abstract

In general, classification is defined as a learning method that classifies data into class labels. It can be perfomed on both structured and unstructured data, based on the data training that has been done. This research leverages Bidirectional LSTM technology in order to develop a news classification model using the CBOW architectures using Word2vec as a word vector. In this research, three main parameters are used: embedding size, window size, and units bilstm. The effects of these three parameters will show optimization of model performance. The results of the constructed model are measured using the accuracy, recall, precision, f1-score and computational time metrics. The findings revealed the greatest performance for title data was for the model with windows size 3, embedding size 200 and unit 128 with 79,18% accuracy. Meanwhile, the data content model has the best performance, on windows size 5, embedding size 300 and units 256 with 92,80% accuracy.
Prototipe Model Generatif dengan LSTM untuk Penciptaan lagu Campur Sari Didi Kempot R Soelistijadi; Eri Zuliarso; Isworo Nugroho
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 4 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i4.2186

Abstract

The campursari music singer Alm. Didi Kempot left a work in the form of 80 albums containing about 700 songs of which 98% of the songs were composed by himself. Therefore, after his death, it was necessary to create a new campursari song so that the existence of the music was maintained. Based on this background, this research tries to build a generative model to produce new songs that have almost the same lyrics as the previous songs. The first stage was taken various song lyrics of campursari as many as 56 song lyrics as data testing then data cleansing was carried out and stored in excel format as the dataset. Then go to the Kaggle platform and use the Pandas library to read the dataset and perform clustering to see the top of terms. The next step using the Keras library is to build a sequential model with the Neural Network architecture on LongShort-TermMemory (LSTM). In order for the system to produce song lyrics as desired, they are generated per line of song lyrics by processing the number of epochs 100 times. The results obtained from this study are in the form of prototypes because in the experimental stage the final results can have many variants of new songs. Therefore, the performance measure used is the level of success in producing new campursari music that has different song lyrics but has the same theme.

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