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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika INFORMAL: Informatics Journal Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan Kasuari: Physics Education Journal (KPEJ) Technologia: Jurnal Ilmiah Jurnal Mnemonic JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Indonesian Journal of Data and Science Aiti: Jurnal Teknologi Informasi Jurnal Pendidikan dan Teknologi Indonesia KLIK: Kajian Ilmiah Informatika dan Komputer
Christian Dwi Suhendra, Christian Dwi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Physics Social Sciences

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Implementasi Geometric Brownian Motion dalam Memprediksi Harga Minyak Mentah pada Masa Pandemi Covid-19 Seru, Feby; Suhendra, Christian Dwi; Saputra, Agung Dwi
PYTHAGORAS Jurnal Pendidikan Matematika Vol 18, No 1: June 2023
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v18i1.49418

Abstract

Minyak mentah atau crude oil memiliki peranan yang vital dalam pertumbuhan ekonomi suatu negara, karena minyak mentah merupakan sumber energi penggerak perekonomian. Untuk menjaga kestabilan perekonomian, maka harga minyak mentah pada periode mendatang perlu diantisipasi dengan cara melakukan prediksi terhadap harga komoditas minyak mentah dunia. Salah satu model yang dapat digunakan untuk memprediksi harga minyak mentah dalam jangka waktu pendek adalah Geometric Brownian Motion (GBM). Tujuan dari penelitian ini adalah mengimplementasikan model GBM dalam memprediksi harga minyak mentah di masa pandemi Covid-19, serta mengukur keakuratan model tersebut. Pada penelitian ini, prediksi harga minyak menggunakan model GBM dilakukan dengan 50, 100, dan 1000 iterasi. Hasil yang diperoleh menunjukkan bahwa model GBM dapat bekerja dengan baik, dalam memprediksi harga minyak mentah di masa pandemi Covid-19. Hal ini ditunjukkan dengan nilai MAPE yang kurang dari 10%. Crude oil has a vital role in the economic growth of a country, because crude oil is a source of energy driving the economy. To maintain economic stability, the price of crude oil in the coming period needs to be anticipated by making predictions on world crude oil commodity prices. One of the models that can be used to predict crude oil prices in the short term is Geometric Brownian Motion (GBM). The purpose of this study is to implement the GBM model in predicting crude oil prices during the Covid-19 pandemic, and to measure the accuracy of the model. In this study, the prediction of oil prices using the GBM model was carried out with 50, 100, and 1000 iterations. The results obtained indicate that the GBM model can work well in predicting crude oil prices during the Covid-19 pandemic. This is indicated by the MAPE value which is less than 10%.
PERANCANGAN SISTEM INFORMASI E¬LAYANAN PENGADUAN DI LLDIKTI WILAYAH XIV PAPUA - PAPUA BARAT BERBASIS PHPRAD Karubui, Raema Miryam; Suhendra, Christian Dwi; Marini, Lion Ferdinand
Jurnal Mnemonic Vol 7 No 2 (2024): Mnemonic Vol. 7 No. 2
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v7i2.9665

Abstract

Tujuan penelitian adalah membuat aplikasi E-Layanan Pengaduan pada LLDIKTI (Lembaga Layanan Pendidikan Tinggi) Wilayah XIV Papua – Papua Barat yang berfungsi sebagai pengumpulan pelaporan berupa dokumen yang disimpan menggunakan teknologi komputer berbentuk dokumen elektronik. Tujuan dari penelitian ini agar memudahkan pengelolaan dokumen persuratan yang ada di Lembaga Layanan Pendidikan Tinggi Wilayah XIV. Metode penelitian yang saya terapkan yaitu RAD (Requirements Planing, Design Workshop, Implementation), Aplikasi E-Layanan Pengaduan ini berbasis website serta menggunakan PHPRAD sebagai program aplikasi kemudian saya juga menggunakan XAMPP server berupa Apache dan MySQL sebagai server lokal. Sedangkan, struktur navigasi digunakan sebagai alur dari suatu program, UML dan DFD digunakan untuk mempresentasikan alur proses kerja antar fungsi. Hasil yang di harapkan dari penelitian ini adalah untuk mempermudah kegiatan pelaporan dan pengarsipan surat yang masuk ke LLDIKTI Wilayah XIV Papua – Papua Barat menjadi lebih efektif dan efisien.
A Machine Learning Perspective on Daisy and Dandelion Classification: Gaussian Naive Bayes with Sobel Suhendra, Christian Dwi; Najwaini, Effan; Maria, Eny; Faizal, Edi
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.112

Abstract

This study explores the classification of Daisy and Dandelion flowers using a Gaussian Naive Bayes classifier, enhanced by Sobel segmentation and Hu moment feature extraction. The research adopted a quantitative approach, utilizing a balanced dataset of Daisy and Dandelion images. The Sobel operator was employed for image segmentation, accentuating the floral features crucial for classification. Hu moments, known for their invariance to image transformations, were extracted as features. The Gaussian Naive Bayes algorithm was then applied, with its performance evaluated through a 5-fold cross-validation process. The results exhibited moderate accuracy, with the highest recorded at 60%, and precision peaking at 62.60%. These findings indicate a reasonable level of effectiveness in distinguishing between the two species, though variations in performance metrics suggested room for improvement. The study contributes to the field of botanical image classification by demonstrating the potential of integrating image processing techniques with machine learning for flower classification. However, it also highlights the limitations of the Gaussian Naive Bayes approach in handling complex image data. Future research directions include exploring more advanced machine learning algorithms and expanding the feature set to enhance classification accuracy. The practical implications of this research extend to ecological monitoring and agricultural studies, where efficient and accurate plant classification is vital
Implementasi Watson Assistant dalam Chatbot Web Budaya dan Peradaban di Tanah Papua Sihite, Rina Wati Nurlia Br; Suhendra, Christian Dwi; Marini, Lion Ferdinand
Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan Vol. 18, No. 3 : Al Qalam (Mei 2024)
Publisher : Sekolah Tinggi Ilmu Al-Qur'an (STIQ) Amuntai Kalimantan Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35931/aq.v18i3.3485

Abstract

Budaya merupakan sebuah fenomena global yang memiliki karakteristik unik, mencerminkan persamaan kodrat manusia dari berbagai suku, bangsa dan ras. Peradaban adalah suatu rangkaian sifat yang dibangun berdasarkan pengetahuan, wawasan kebangsaan dan nilai suatu budaya. Hubungan manusia dengan peradaban sangat erat karena saling bergantung untuk membentuk suatu kehidupan yang hakikat. Namun pada saat ini sebagian besar pengenalan terhadap kebudayaan masih dilakukan dengan metode konvensional seperti pada buku pelajaran, seminar, pertunjukan, dan pameran. Hal ini menunjukkan bahwa efektivitas kegiatan pengenalan belum mencapai tingkat yang optimal. Penelitian ini akan memanfaatkan teknologi untuk memberikan layanan otomatis dalam menjawab pertanyaan umum seputar kebudayaan dan peradaban di Tanah Papua dengan memanfaatkan data yang telah dikumpulkan. Teknologi ini dikenal dengan chatbot yang menggunakan platform IBM Watson Assistant dengan model Natural Language Processing (NLP). Chatbot yang dibuat akan diimplementasikan pada sebuah situs web yang dapat diakses oleh seluruh pengguna tanpa batasan tempat dan waktu. Hasil yang diperoleh dari chatbot dengan menggunakan model NLP dapat memberikan respon yang valid terhadap 100 pertanyaan yang dimasukkan. Setelah dilakukan proses reformulasi pertanyaan, chatbot mampu memberikan jawaban sesuai dengan yang diharapkan dengan tingkat akurasi 100%.
Implementasi Metode Prototype dalam Pengembangan Aplikasi Wondama-Tourism Berbasis Android Albab, Elfan Ulil; Suhendra, Christian Dwi; Marini, Lion Ferdinand
Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan Vol. 18, No. 2 : Al Qalam (Maret 2024)
Publisher : Sekolah Tinggi Ilmu Al-Qur'an (STIQ) Amuntai Kalimantan Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35931/aq.v18i2.3394

Abstract

Kabupaten Teluk Wondama mempunyai potensi wisata yang luas mulai dari wisata budaya, religi dan alam. Setiap tahunnya Kabupaten Teluk Wondama menerima wisatawan dari berbagai daerah dan mancanegara untuk mengunjungi tujuan wisata baik budaya, alam, maupun religi. Selain itu dinas pariwisata Kabupaten Teluk Wondama juga sering mengadakan festival diantaranya adalah festival pulau Roon. Pengelolaan manajemen pariwisata yang dilakukan dengan cara biasa atau belum terdigitalisasi membuat potensi-potensi wisata tersebut kurang banyak menarik minat wisatawan, dan promosi destinasi wisata cenderung terbatas. Aplikasi Wondama-Tourism dibangun menggunakan metode prototyping. Terdapat 5 tahapan pada metode prototyping yang terdiri dari analisis kebutuhan, konstruksi prototyping, evaluasi prototyping, pengkodean, pengujian, evaluasi, dan penggunaan sistem. Penelitian ini menghasilkan 2 Mockup. Kedua Mockup ini menjadi langkah awal dalam memperbaiki infrastruktur pariwisata kabupaten Teluk Wondama hingga memenuhi kebutuhan fungsional dinas pariwisata kabupaten Teluk Wondama. Proses pengujian menggunakan Blackbox testing terdapat ada 26 fitur yang di uji. Selain validasi fungsionalitas, Pengujian juga memeriksa sejauh mana fitur dapat beroperasi sesuai dengan kebutuhan dinas pariwisata Kabupaten Teluk Wondama. Hasil dari pengujian menunjukan bahwa semua fitur yang di uji dapat berjalan dengan baik.
Prediksi Mahasiswa Baru Universitas Papua Menggunakan Autoregressive Integrated Moving Average Suhendra, Christian Dwi; Marini, Lion Ferdinand; Sarungallo, Ana
Jurnal Informatika Vol 10, No 2 (2023): October 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i2.16637

Abstract

Perguruan tinggi sangat memperhatikan penerimaan mahasiswa baru sebagai indikator kemajuan dan pertumbuhan institusi. Namun, fluktuasi jumlah penerimaan mahasiswa baru dapat menjadi kendala dalam perencanaan dan pengambilan keputusan. Penelitian ini bertujuan untuk menerapkan metode Autoregressive Integrated Moving Average (ARIMA) dalam menghitung prediksi jumlah mahasiswa baru pada Universitas Papua. Oleh karena itu, penelitian ini menggunakan metode ARIMA untuk meramalkan jumlah mahasiswa baru tahun 2023 dengan memanfaatkan data historis dari tahun 2017 hingga 2022. Hasil analisis menggunakan R Studio menunjukkan bahwa model terbaik adalah ARIMA (11,0,12) dengan nilai error terendah. Penelitian ini memberikan manfaat bagi peneliti dalam memahami metode peramalan menggunakan ARIMA dan juga memberikan informasi yang berguna bagi Universitas Papua dalam perencanaan strategis untuk meningkatkan kualitas dan minat calon mahasiswa baru. Universities pay great attention to the acceptance of new students as an indicator of the progress and growth of the institution. However, fluctuations in the number of new student admissions can be an obstacle in planning and decision making. This study aims to apply the Autoregressive Integrated Moving Average (ARIMA) method in calculating predictions for the number of new students at the University of Papua. Therefore, this study uses the ARIMA method to predict the number of new students in 2023 by utilizing historical data from 2017 to 2022. The results of the analysis using R Studio show that the best model is ARIMA (11,0,12) with the lowest error value. This research provides benefits for researchers in understanding forecasting methods using ARIMA and also provides useful information for the University of Papua in strategic planning to improve the quality and interest of prospective new students. 
Sistem penilaian kinerja pegawai di kantor Kementerian Agama Provinsi Papua Barat menggunakan metode Self Organizing Map (SOM) Jubita, Jubita; Suhendra, Christian Dwi; Sanglise, Marlinda
AITI Vol 21 No 1 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i1.96-116

Abstract

A government agency requires employees who have competence and good performance. Various factors affect performance between the abilities of other individuals and the agency environment, including the office of the Ministry of Religion in West Papua Province. However, the performance assessment process still needs to be better for making assessment decisions and is still subjectively based. Those things affect the employee's assessments. For example, employees cannot complete work according to predetermined targets and must be more careful in carrying out the work. So, to facilitate this assessment, an accurate calculation application is needed. A Self Organizing Map (SOM) is used to sort data in a group with similar data that are close to each other. Using the R (programming language) and R studio as the required application platform, we can calculate values and form them into three clusters: very good, quite good, and poor. Then, from the calculation results, 55 employees, with a percentage of 42%, match cluster with quite good performance, 29 employees, with a percentage of 22%, are in cluster with poor performance, and 48 employees, with a percentage of 36%, are in cluster with very good performance.
Analisis Peramalan Harga Telur Ayam Ras Dengan Menggunakan Metode SARIMA Hakim, Ilham Luqman; Sanglise, Marlinda; Suhendra, Christian Dwi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7610

Abstract

Eggs are a widely favored source of animal protein. One commonly consumed type is chicken eggs. Besides being easy to prepare, a notable advantage of chicken eggs is their affordability compared to other sources of animal protein. However, chicken eggs are subject to price fluctuations at specific times, such as religious holidays. These fluctuations can lead to losses for both producers and consumers. One way to address this issue is by forecasting. Forecasting is crucial as it provides estimated price information for the future. With this information, producers and consumers can prepare appropriate strategies to cope with price changes. The price of chicken eggs fluctuates periodically, thus the method employed in this research is Seasonal Autoregressive Integrated Moving Average (SARIMA). SARIMA is a forecasting method specifically used for data exhibiting seasonal patterns. Based on the results of this study, the best SARIMA model obtained is , demonstrating excellent performance with an RMSE value of 1491.30 and a MAPE of 3.40%. From this model, forecasted prices of chicken eggs in the traditional market of Manokwari city for the next 12 months are obtained, spanning from March 2024 to February 2025. According to the forecast results, the price of chicken eggs is expected to rise in March to June 2024 and December 2024 to January 2025.
Implementasi Naïve Bayes pada Sistem Pakar untuk Deteksi Dini Penyakit Liver Asri, Windi Fahmi; Sanglise, Marlinda; Suhendra, Christian Dwi
INFORMAL: Informatics Journal Vol 9 No 1 (2024): INFORMATICS JOURNAL (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i1.34710

Abstract

Liver health is very important for the human body. The liver as an organ that has the main task of neutralizing toxins in the body makes toxins that have been entering through our bodies from food or the environment can be neutralized by the liver. Based on the results of interviews conducted online to dr. Felix who works at Bhayangkara Hospital, one of the problems that occurs in the medical world is that people who are not medically trained if they experience symptoms of liver disease do not understand the ways to overcome them or the solution. If ignored, liver problems can get worse and more difficult to treat. Therefore, to overcome these problems, development is carried out by creating an expert system that can detect liver disease early by implementing the Naïve Bayes Method on the expert system. The expert system was built using the Rapid Application Development (RAD) development method. By making a design consisting of use cases, activities, class diagrams, database design and user interface design. Implementation in this expert system uses the PHP programming language and MySQL database. After the system is successfully created, the system is tested using the blackbox testing method which shows that the functionality of the expert system is running well and testing the user acceptance test (UAT) by distributing questionnaires to respondents to get the percentage of feasibility of 76.7% with a suitable category for use
Analisis Data Time Series Untuk Prediksi Harga Komoditas Pangan Menggunakan Autoregressive Integrated Moving Average Sihombing, Ester Ivo; Suhendra, Christian Dwi; Marini, Lion Ferdinand
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1863

Abstract

Onions and chilies are two of the many food commodities frequently used by Indonesian people in their daily lives. The high demand in the market leads to price instability, causing prices to fluctuate or remain unstable. This can result in farmers suffering losses when selling their agricultural products. Therefore, forecasting is conducted to predict future prices of onions and chilies. This can provide information on the estimated prices that farmers will set for sale to traders, which is expected to address market price instability. This research aims to obtain the best model from the Autoregressive Integrated Moving Average (ARIMA) for forecasting the prices of onions and chilies in Manokwari Regency in 2024. The data for this study is sourced from the SP2KP (Market and Basic Needs Monitoring System) website, consisting of price data for red onions, garlic, and bird's eye chilies from January 2016 to December 2023. The best ARIMA models based on the smallest AIC values are ARIMA (2,0,0) with an AIC of 1341.784, ARIMA (3,0,0) with an AIC of 1278.688, and ARIMA (1,0,0) with an AIC of 1466.834 for red onions, garlic, and bird's eye chilies respectively, with RMSE values of 7447.06, 3501.71, and 13787.59 respectively. From these models, the predicted prices of the three commodities in 2024 from January to December are as follows: red onions around Rp 50,000/kg, garlic around Rp 40,000/kg, and bird's eye chilies between Rp 50,000 and Rp 70,000/kg
Co-Authors Albab, Elfan Ulil Asri, Windi Fahmi Edi Faizal Effan Najwaini FEBY SERU Hakim, Ilham Luqman Heremba, Juleha Irianti Jubita, Jubita Karubui, Raema Miryam Lantang, Stefanie Stanly Maria, Eny Marini, Lion Ferdinand Pramanagara, Rio Octovinary Sanglise, Marlinda Saputra, Agung Dwi Sarungallo, Ana Sihite, Rina Wati Nurlia Br Sihombing, Ester Ivo Yati, Fatimah Aszhara Yonathan, Alberto Yonathan Tangke