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All Journal Prosiding SNATIKA Vol 01 (2011) JUITA : Jurnal Informatika Creative Information Technology Journal Jurnal Sains dan Informatika ILKOM Jurnal Ilmiah JAMI: Jurnal Ahli Muda Indonesia Advance Sustainable Science, Engineering and Technology (ASSET) Joutica : Journal of Informatic Unisla JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer Jurnal Rekam Medis dan Informasi Kesehatan Indonesia
Risa Helilintar
Teknik Informatika, Fakultas Teknik, Universitas Nusantara PGRI Kediri
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Public Health Social Sciences

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Classification of Dog and Cat Images using the CNN Method Teguh Adriyanto; risky aswi ramadhani; Risa Helilintar; Aidina Ristyawan
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1116.203-208

Abstract

Blind people can be defined as those people who are unable to see objects or pictures around them with their eyes. This inability becomes an issue for them when dealing with objects or images in front of them. These problems lead to the novelty of this study that is to recognize objects or images around blind people with the CNN algorithm. Dogs and cats were used as objects in this study. These object recognitions used Deep Learning, a relatively new science in the field of machine learning. Deep learning works like the human brain's ability to recognize an object. In this study, the objects that were used were pictures of a dog and a cat. This study used 3 types of data, namely training, validation, and testing data. The data training consisted of dog data with a total of 1000 images and cat data with a total of 1000 images. Data validation consisted of 500 dog data  and 500 cat data. The CCN architecture employed 3 convolution layers. The layer was convolution 1 using 16 filters of kernel size 3x3, the second convolution using 32 filters of  kernel size 3x3 and the third using 64 filters of kernel size 3x3. While the data testing consisted of 51dog data and 27 cat data. The method used to analyze the image was CNN. The input was an image with a size of 150x150 pixels with 3 channels, namely R, G, and B. This classification went through a performance test with the Confusion Matrix and it obtained 45% precision, 45% recall and 45% f1-score. From these results it can be concluded that the accuracy values should be improved.
Facebook Prophet Model with Bayesian Optimization for USD Index Prediction Ahmad Fitra Hamdani; Daniel Swanjaya; Risa Helilintar
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17880

Abstract

Accuracy is the primary focus in prediction research. Optimization is conducted to improve the performance of prediction models, thereby enhancing prediction accuracy. This study aims to optimize the Facebook Prophet model by performing hyperparameter tuning using Bayesian Optimization to improve the accuracy of USD Index Value prediction. Evaluation is conducted through multiple prediction experiments using different ranges of historical data. The results of the study demonstrate that performing hyperparameter tuning on the Facebook Prophet model yields better prediction results. Prior to parameter tuning, the MAPE indicator metric is 1.38% for the historical data range of 2014-2023, and it decreases to 1.33% after parameter tuning. Further evaluation shows improved prediction performance using different ranges of historical data. For the historical data range of 2015-2023, the MAPE value decreases from 1.39% to 1.20%. Similarly, for the data range of 2016-2023, the MAPE decreases from 1.12% to 0.80%. Furthermore, for the data range of 2017-2023, there is a decrease from 0.80% to 0.76%. This is followed by the data range of 2018-2023, with a decrease from 0.75% to 0.70%. Lastly, for the data range of 2019-2023, there is a decrease from 0.63% to 0.55%. These results demonstrate that performing Hyperparameter Optimization using Bayesian Optimization consistently improves prediction accuracy in the Facebook Prophet model.
Sistem Pendukung Keputusan Kinerja Karyawan Terbaik Dengan Menggunakan Kombinasi Metode TOPSIS Dan Metode ROC Ahnan, Miftah; Farida, Intan Nur; Helilintar, Risa
JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer Vol. 2 No. 1 (2023): Desember 2023
Publisher : Cipta Media Harmoni

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53624/jsitik.v2i1.255

Abstract

Persaingan dalam dunia bisnis semakin kompetitif sehingga untuk meningkatkan kualitas dari yayasan perlu upaya yang cukup keras. Karyawan merupakan aspek penting dalam aktivitas bisnis. Dalam hal ini Yayasan Sahhala Kota Kediri yang fokus pada pengasuhan anak yatim piatu dan sekolah alam ingin melakukan pemberian bonus kepada karyawan yang memiliki kinerja bagus, sehingga memberikan motivasi karyawan lain agar bekerja lebih maksimal. Namun dalam hal ini Yayasan Sahhala memiliki kendala dalam melakukan seleksi karyawan terbaik. Untuk mengatasi masalah tersebut, penelitian ini menggunakan metode Technique For Order Preference By Similarity To Ideal Solution (TOPSIS) dengan pembobotan Rank Order Centroid (ROC) untuk menentukan bobot sesuai dengan kriteria-kriteria yang memiliki prioritas tinggi. Hasil dari penelitian ini yaitu seleksi karyawan terbaik dengan metode Technique For Order By Similarity To Ideal Solution (TOPSIS) berdasarkan 5 variabel kriteria. Dari hasil dapat disimpulkan yaitu sistem seleksi karyawan terbaik dapat membantu untuk seleksi karyawan pada Yayasan Sahhala sehingga dapat memilih karyawan terbaik sesuai dengan kinerjanya.
SISTEM PENENTUAN KELAS UNGGULAN PADA SISWA SMP MENGGUNAKAN METODE NAÏVE BAYES Anggakara, Agra; Helilintar, Risa; Ramadhani, Risky Aswi
JAMI: Jurnal Ahli Muda Indonesia Vol. 4 No. 1 (2023): Juni 2023
Publisher : Akademi Komunitas Negeri Putra Sang Fajar Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46510/jami.v4i1.110

Abstract

Objektif. Penelitian ini bertujuan untuk menganalisis dan mengklasifikasi anggota kelas unggulan di SMP Katholik Mardiwiyata Kota Kediri. Penelitian ini tergolong pada penelitian aplikatif karena di dalamnya terdapat informasi yang lengkap tentang klasifikasi siswa yang lolos kelas unggulan, maka penelitian ini disusun menggunakan konsep Sistem Development Life Cycle. Sumber data dalam penelitian ini diperoleh secara langsung dengan melakukan wawancara pada salah satu guru SMP Katholik Mardiwiyata Kota Kediri. Jumlah total siswa SMP Katholik Mardiwiyata yaitu ada 260 siswa, dan jumlah siswa kelas 7 saat ini berjumlah 85siswa. Penentuan kelas unggulan ditentukan beberapa kriteria atau variabel, yaitu: Nilai Harian, UTS, UAS, Nilai Keterampilan, Nilai Spiritual dan Nilai Sosial pada kelas 7 semester gasal dan semester genap. Material and Metode. Naïve Bayes salah satu algoritma klasifikasi berdasarkan teorema pada statistika. Naive Bayes merupakan pengklasifikasian dengan metode probabilitas dan statistik, merupakan teknik prediksi berbasis probabilistik sederhana yang berdasarkan pada penerapan teorema Bayes dengan asumsi independensi yang kuat (Naif). Naive Bayes, poin penting tentang independensi fungsi yang kuat bahwa fungsi dalam data tidak terkait dengan ada atau tidak adanya fungsi lain dalam data yang sama Hasil. Hasil pengujian sistem menggunakan confusion matrix diperoleh precision: 75%, recall: 85% dan accuracy: 82% Kesimpulan. Dari hasil uji coba Sistem Penentuan Kelas Unggulan Pada Siswa SMP Menggunakan Metode Naïve Bayes dengan confusion matrix didapatkan akurasi 82%. Hal ini menunjukkan sistem sudah berjalan dengan baik, karena nilai presisi, recall dan akurasi berimbang, namun pada penelitian kedepan sistem ini perlu perbaikan lebih lanjut sampai nilai presisi, recall dan akurasinya diatas 90% Objektif. Penelitian ini bertujuan untuk menganalisis dan mengklasifikasi anggota kelas unggulan di SMP Katholik Mardiwiyata Kota Kediri. Penelitian ini tergolong pada penelitian aplikatif karena di dalamnya terdapat informasi yang lengkap tentang klasifikasi siswa yang lolos kelas unggulan, maka penelitian ini disusun menggunakan konsep Sistem Development Life Cycle. Sumber data dalam penelitian ini diperoleh secara langsung dengan melakukan wawancara pada salah satu guru SMP Katholik Mardiwiyata Kota Kediri. Jumlah total siswa SMP Katholik Mardiwiyata yaitu ada 260 siswa, dan jumlah siswa kelas 7 saat ini berjumlah 85siswa. Penentuan kelas unggulan ditentukan beberapa kriteria atau variabel, yaitu: Nilai Harian, UTS, UAS, Nilai Keterampilan, Nilai Spiritual dan Nilai Sosial pada kelas 7 semester gasal dan semester genap. Material and Metode. Naïve Bayes salah satu algoritma klasifikasi berdasarkan teorema pada statistika. Naive Bayes merupakan pengklasifikasian dengan metode probabilitas dan statistik, merupakan teknik prediksi berbasis probabilistik sederhana yang berdasarkan pada penerapan teorema Bayes dengan asumsi independensi yang kuat (Naif). Naive Bayes, poin penting tentang independensi fungsi yang kuat bahwa fungsi dalam data tidak terkait dengan ada atau tidak adanya fungsi lain dalam data yang sama Hasil. Hasil pengujian sistem menggunakan confusion matrix diperoleh precision: 75%, recall: 85% dan accuracy: 82% Kesimpulan. Dari hasil uji coba Sistem Penentuan Kelas Unggulan Pada Siswa SMP Menggunakan Metode Naïve Bayes dengan confusion matrix didapatkan akurasi 82%. Hal ini menunjukkan sistem sudah berjalan dengan baik, karena nilai presisi, recall dan akurasi berimbang, namun pada penelitian kedepan sistem ini perlu perbaikan lebih lanjut sampai nilai presisi, recall dan akurasinya diatas 90%
PERANCANGAN DESAIN FORMULIR PERSETUJUAN TINDAKAN KEDOKTERAN ELEKTRONIK PASIEN RAWAT INAP DI UPTD PUSKESMAS TANJUNGANOM KABUPATEN NGANJUK Deni Luvi Jayanto; Muhammad Mutafanninun; Nurhadi Nurhadi; Risa Helilintar
Jurnal Rekam Medis dan Informasi Kesehatan Indonesia Vol. 2 No. 2 (2023): Jurnal Rekam Medis dan Informasi Kesehatan Indonesia
Publisher : program studi Rekam Medis dan Infomasi Kesehatan ITSK RS dr Soepraoen Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/jurmiki.v2i2.34

Abstract

Doctors at the Tanjunganom Health Center UPTD use a medical action approval form before taking medical action. Baseded on the findings, the implementation of the medical action approval form at the Tanjunganom Health Center UPTD was difficult to read and the writing was not in accordance with the rules for documenting medical records. Whereas approval of medical action as evidence of legality in court and avoiding malpractice actions that harm patients. The purpose of the study was to design an electronic medical action approval form according to the needed of the officer, based on color, layout and control. This study used a qualitative descriptive, for the population and a sample of 2 inpatient registration officers who use informed consent. The result of this research is the interface design of the electronic medical action consent form. The results of interviews with 2 inpatient registration officers obtained that the colour uses light green as a colour symbol from the Tanjunganom Health Center, the layout was neatly arranged according to the comfort of the officers when using the application, and the controls used are icons and explanatory text adjusted to their functions so that the use of buttons was easy. understood. These results are adjusted to the needed of officers to make it easier for officers to operate the application. It should be realized immediately by organizing training, outreach to officers so that services are more effective and efficient.
Optimizing Predictive Accuracy: A Study of K-Medoids and Backpropagation for MPX2 Oil Sales Forecasting Ramadhan, Ryan Akbar; Swanjaya, Daniel; Helilintar, Risa
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17665

Abstract

This study evaluates the use of K-Medoids and Backpropagation methods for predicting MPX2 Oil sales in the automotive workshop industry, which is crucial for meeting customer demands and refining sales strategies. Utilizing transaction data from 2022 to 2023, the study involves normalizing and processing this data with these algorithms to forecast stock levels, focusing on accuracy measures such as Mean Absolute Deviation (MAD) and Mean Squared Error (MSE). K-Medoids assist in identifying customer purchase patterns through clustering, while Backpropagation effectively predicts sales trends, enhancing accuracy through training. Implementing K-Medoids and Backpropagation algorithms in the research resulted in  MSE value of 0.01969 and  MAD value of 0.12200. These values indicate a high level of accuracy in the MPX2 Oil sales predictive model, as lower MSE and MAD values suggest greater accuracy and precision in forecasting. These findings provide valuable insights into the dynamics of MPX2 Oil sales, enabling companies to improve marketing strategies, transaction management, and inventory strategies.
Co-Authors Abdurrahman Secondanu Mustakim Abimanyu Agung Saputro Achmad Zainul Karim Affandi Febrinsa Pratama Agra Anggakara Ahmad Bagus Setiawan Ahmad Fitra Hamdani Ahmad Jamaludin Ahnan, Miftah Aidina Ristyawan Alfin Aziema Alfino Wahyu Pramudya Anggakara, Agra Ani Asmawati Tani Antika, Firma Fuji Rinti Anwar Muzaki Ardi Sanjaya Bagas Dewantara Bahtiar, Miftahul Ilmi Big Daya Yudha Asmara Danang Wahyu Widodo Danar Putra Pamungkas, Danar Putra Dandyadex Dandyade Candra Daniel Swanjaya Deni Luvi Jayanto Eka Yuniarti Erwanto, Rio Aldi Fakhry Miftakhul Huda Fawaid, Muhammad Hasib Gadang Putro Bagus Setiyawan Gusnugraeni, Alifdyah Hermasrurin Hanif Al Fatta Haris Yulianto Heru Setiyawan Ida Ayu Putu Sri Widnyani Intan Nur Farida Inzaghi, Febri Wika Jamhari Jamhari Juhana Lillasari Julian Sahertian Karina Ananda Putri Khamdanni, Moh. M.Herma Pradipta Made Ayu Dusea Widyadara Made Ayu Dusea Widyadara - Universitas Nusantara Kediri, Made Ayu Dusea Widyadara Mahdiyah, Umi Maulana Anas Firdaus Mido, Gafana Oly Moch Nur Hudha Mochamad Syafroni Muhammad Mutafanninun Muhammad Nur Fachrudin Muhammat Arisona Firmansah Mukhtari, Nailusofa Al Muzan Ihda Khotmuniza Nugraha, Yoga Adi Nurhadi Nurhadi Patmi Kasih Prasetya, Marsha Auriel Ramadhan, Ryan Akbar Ratih Kumalasari Niswatin Resty Wulaningrum Resty Wulanningrum Reza Mawarni Rini Indriati Risky Aswi R, Risky Rizki Dwi Febrian Rochana, Siti Rochana, Siti Rochana, Siti Rony Heri Irawan Salma Putri Awalina Santoso, Ricky Laschka Zidane Siti Rochana Siti Rochana Soim Arifin Teguh Adriyanto Uun Hidayat Widyadara , Made Ayu Dusea Wulandari, Miftakhul Yustikawan, Eko Tri