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All Journal JOIV : International Journal on Informatics Visualization Tech-E JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Informatics and Communication Technology (JICT) IT For Society : Journal of Information Technology Jurnal Rekayasa elektrika
Deny Haryadi
Universitas Telkom
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Mathematics

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Penerapan Algoritma K-Means Clustering Pada Produksi Perkebunan Kelapa Sawit Menurut Provinsi Deny Haryadi
Journal of Informatics and Communication Technology (JICT) Vol 3 No 1 (2021)
Publisher : PPM Institut Teknologi Telkom Telkom Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (491.106 KB) | DOI: 10.52661/j_ict.v3i1.71

Abstract

Kelapa sawit merupakan salah satu perkebunan yang produknya berpengaruh menjadi salah satu komoditas ekspor utama indonesia. Tingginya produksi perkebunan kelapa sawit dipengaruhi oleh adanya permintaan akan kebutuhan konsumsi minyak nabati dunia. Selain itu, tingginya produksi perkebunan kelapa sawit memiliki peranan besar terhadap perekonomian nasional terutama dari sisi penerimaan pajak dan pundi-pundi devisa negara. Tujuan dari penelitian ini untuk pengelompokan provinsi-provinsi yang menjadi prioritas terhadap produksi perkebunan kelapa sawit di indonesia. Berdasarkan hasil pengujian yang telah dilakukan dalam penelitian ini menggunakan algoritma K-Means Clustering yaituCluster 1 merupakan kategori provinsi dengan produksi perkebunan kelapa sawit rendah atau Low yaitu 14 (aceh, sumatera barat, bengkulu, lampung, kep.bangka belitung, kep.riau, jawa barat, banten, kalimantan selatan, sulawesi tengah, sulawesi selatan, sulawesi barat, papua barat dan papua) dari 21 kategori provinsi yang diuji, kemudian cluster 2 adalah kategori provinsi dengan produksi perkebunan kelapa sawit sedang atau Medium yaitu 4 (jambi, sumatera selatan, kalimantan barat, dan kalimantan timur) dari 21 kategori provinsi yang diuji, dan terakhir adalah cluster 3 merupakan kategori provinsi dengan produksi perkebunan kelapa sawit tinggi atau High yaitu 3 (sumatera utara, riau, dan Kalimantan tengah) dari 21 kategori provinsi yang diuji.
Integrasi Learning Management System dan Database Eksternal Menggunakan Oracle Studi Kasus: IT Telkom Jakarta Demi Adidrana; Deny Haryadi; Seandy Arandiant Rozano
Journal of Informatics and Communication Technology (JICT) Vol 3 No 2 (2021)
Publisher : PPM Institut Teknologi Telkom Telkom Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1543.921 KB) | DOI: 10.52661/j_ict.v3i2.80

Abstract

IT Telkom Jakarta adalah perguruan tinggi swasta milik Yayasan Pendidikan Telkom yang merupakan transformasi dari Akademi Teknik Telekomunikasi Sandhy Putra Jakarta atau yang lebih dikenal sebagai Akademi Telkom Jakarta. Saat ini, IT Telkom Jakarta mempunyai Learning Management System (LMS) yang dikembangkan menggunakan moodle. Selain LMS, IT Telkom Jakarta memiliki suatu sistem informasi akademik terintegrasi yang dinamakan iGracias. Dengan memanfaatkan database dari iGracias yang menggunakan Oracle maka dilakukan pengintegrasian data iGracias ke dalam LMS, sehingga seluruh data di LMS akan menggambil dari database eksternal yang merupakan cerminan dari iGracias dan untuk mengaksesnya harus menggunakan akun SSO dari iGracias. Berdasarkan hasil penelitian, integrasi berhasil dilakukan dengan melakukan beberapa tahapan seperti menganalisa sistem, membuat view database di external database berdasarkan kebutuhan dari LMS, melakukan installasi driver databse oci8 dan konfigurasi dari sisi server LMS. Mapping fields eksternal database berhasil dilakukan dengan melakukan test setting. Ujicoba keseluruhan berhasil dilakukan dan dibuktikan dengan adanya data pada external database yaitu terdaftar 2321 user dan 187 course dan 3695 data enrolments, serta berhasil melakukan login menggunakan SSO iGracias.
Penerapan Algoritma K-Means Clustering Untuk Pengelompokan Tingkat Risiko Penyakit Jantung Deny Haryadi; Dewi Marini Umi Atmaja
Journal of Informatics and Communication Technology (JICT) Vol 3 No 2 (2021)
Publisher : PPM Institut Teknologi Telkom Telkom Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.041 KB) | DOI: 10.52661/j_ict.v3i2.85

Abstract

Penyakit jantung merupakan sebuah kondisi dimana jantung tidak dapat melaksanakan tugasnya dengan baik, penyakit ini terjadi bila darah ke otot jantung terhenti atau tersumbat sehingga mengakibatkan kerusakan berat pada jantung. Beberapa faktor yang menyebabkan penyakit jantung antara lain keturunan, usia, jenis kelamin, stres, kurang gerak, merokok, kolesterol tinggi, hipertensi, diabetes, dan obesitas. Pada dasarnya penyakit jantung dapat dicegah dengan berbagai faktor diantaranya pola hidup sehat, selain itu deteksi dini penyakit jantung juga diperlukan untuk mencegah terjadinya kematian pada penderitanya salah satu cara untuk melakukan deteksi dini ialah menggunakan data mining. Penggunaan algoritma k-means dapat dilakukan untuk melakukan klasterisasi pengelompokan penyakit jantung guna mengetahui seseorang terkena penyakit jantung maupun tidak. Metode klasterisasi dengan algoritma k-means pada penelitian ini menunjukkan sebuah wawasan baru yaitu pengelompokkan tingkat resiko penyakit jantung berdasarkan 3 cluster. Cluster 1 merupakan kategori usia dengan tingkat resiko penyakit jantung cukup rendah atau Low yaitu 355 dari 1025 kategori usia yang diuji, kemudian cluster 2 adalah kategori usia dengan tingkat resiko penyakit jantung sedang atau Medium yaitu 208 dari 1025 kategori usia yang diuji, dan terakhir adalah cluster 3 merupakan kategori usia dengan tingkat kategori usia cukup tinggi atau High yaitu 462 dari 1025 kategori usia yang diuji.
Implementation of Support Vector Regression for Polkadot Cryptocurrency Price Prediction Deny Haryadi; Arif Rahman Hakim; Dewi Marini Umi Atmaja; Syifa Nurgaida Yutia
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.945

Abstract

Cryptocurrency investment is an investment instrument that has high risk but also has a greater advantage than other investment instruments. To make a big profit, investors need to analyze cryptocurrency investments to predict the price of the cryptocurrency to be purchased. The highly volatile movement of cryptocurrency prices makes it difficult for investors to predict those prices. Data mining is the process of extracting large amounts of information from data by collecting, using data, the history of data relationship patterns, and relationships in large data sets. Support Vector Regression has the advantage of doing accurate cryptocurrency price predictions and can overcome the problem of overfitting by itself. Polkadot is one of the cryptocurrencies that are often used as investment instruments in the world of cryptocurrencies. Polkadot cryptocurrency price prediction analysis using the Support Vector Regression algorithm has a good predictive accuracy value, including for Polkadot daily closing price data, namely with a radial basis function (RBF) kernel with cost parameters C = 1000 and gamma = 0.001 obtained model accuracy of 90.00% and MAPE of 5.28 while for linear kernels with parameters C = 10 obtained an accuracy of 87.68% with a MAPE value of 6.10. It can be concluded that through parameter tuning, the model formed has an accuracy value and the best MAPE is to use a radial kernel basis function (RBF) with cost parameters C = 1000 and gamma = 0.001. The results show that the Support Vector Regression method is quite good if used for the prediction of Polkadot cryptocurrencies.
Prediksi Harga Minyak Kelapa Sawit Dalam Investasi Dengan Membandingkan Algoritma Naïve Bayes, Support Vector Machine dan K-Nearest Neighbor Deny Haryadi; Rila Mandala
IT for Society Vol 4, No 1 (2019)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (440.049 KB) | DOI: 10.33021/itfs.v4i1.1181

Abstract

Harga minyak kelapa sawit bisa mengalami kenaikan, penurunan maupun tetap setiap hari karena faktor yang mempengaruhi harga minyak kelapa sawit seperti harga minyak nabati lain (minyak kedelai dan minyak canola), harga minyak mentah dunia, maupun nilai tukar riil antara kurs dolar terhadap mata uang negara produsen (rupiah, ringgit, dan canada) atau mata uang negara konsumen (rupee). Untuk itu dibutuhkan prediksi harga minyak kelapa sawit yang cukup akurat agar para investor bisa mendapatkan keuntungan sesuai perencanaan yang dibuat. tujuan dari penelitian ini yaitu untuk mengetahui perbandingan accuracy, precision, dan recall yang dihasilkan oleh algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbor dalam menyelesaikan masalah prediksi harga minyak kelapa sawit dalam investasi. Berdasarkan hasil pengujian dalam penelitian yang telah dilakukan, algoritma Support Vector Machine memiliki accuracy, precision, dan recall dengan jumlah paling tinggi dibandingkan dengan algoritma Naïve Bayes dan algoritma K-Nearest Neighbor. Nilai accuracy tertinggi pada penelitian ini yaitu 82,46% dengan precision tertinggi yaitu 86% dan recall tertinggi yaitu 89,06%.
Identifikasi Kebangkrutan Perusahaan Menggunakan Algoritma Regresi Linear Berganda Deny Haryadi; Arif Rahman Hakim; Dewi Marini Umi Atmaja; Amat Basri; Risma Adisty Nilasari
Tech-E Vol. 6 No. 2 (2023): The Tech-E Journal Vol. 6 No. 2 publishes research papers in such informatics:
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

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

Abstract

Corporate bankruptcy can hurt the company and affect the state of the economy. Therefore, many interested parties want to know the business situation related to the company. These parties include creditors, auditors, shareholders, and management itself who have an interest in knowing the state of the company in the context of bankruptcy. The past financial statements of a company can be used to predict future financial conditions using report analysis techniques. In the risk assessment process, expert knowledge is still seen as an important task, because expert predictions are subjective. This study aims to predict the bankruptcy of the company using influencing factors such as the level of research and development costs, the growth rate of total assets, and the current asset turnover rate. The method used in this research is the prediction method using the Linear Regression Algorithm. Based on the test results show that the variables or attributes used in this study have a significant effect, as evidenced by using a linear regression algorithm to be able to produce a Root Mean Squared Error value: 0.162 +/- 0.000.
IDENTIFY CHOLESTEROL DISEASE RISK LEVELS USING MULTIPLE LINEAR REGRESSION ALGORITHMS Haryadi, Deny; Umi Atmaja, Dewi Marini
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 8 No 1 (2022): JITK Issue August 2022
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1474.806 KB) | DOI: 10.33480/jitk.v8i1.3328

Abstract

Cholesterol is one of the fat compounds found in the bloodstream that are necessary for the formation of several hormones and new cell walls in the liver. Normal cholesterol levels in the human body are in the range of < 200 mg / dL. If cholesterol levels in the blood are abnormal or excessive, it can result in dangerous diseases such as heart disease or stroke. In this study, cholesterol disease prediction will be carried out using models formed from linear regression methods, so that the results of this study can be used as a reference for early prevention of cholesterol disease and become a means of decision making. Linear regression is one of the prediction methods in data mining that can be used to find out how dependent variables/criteria can be predicted through independent variables or predictor variables individually. In this study by utilizing some data of patients with cholesterol disease that has been stored in the database using several attributes, namely age, BMI, glucose, and cholesterol. So by applying a linear regression algorithm can be done a prediction in the identification of cholesterol diseases based on functional relationships on the attributes in the data. The results of this study showed an RMSE value of 0.347 with a standard deviation of /- 0.000. This shows that the model resulting from linear regression algorithms with the above cases is quite accurate.
Identifikasi Citra Kualitas Minyak Kelapa Sawit Berbasis Android Menggunakan Algoritma Convolutional Neural Network Deny Haryadi; Sasmi Hidayatul Yulianing Tyas; Adi Kuncoro; Fiqry Firdhan Pratama Putra; Andri Ariyanto
Jurnal Rekayasa Elektrika Vol 18, No 4 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (882.601 KB) | DOI: 10.17529/jre.v18i4.28617

Abstract

The Central Statistics Agency reports that the average development of palm cooking oil consumption at the household level in Indonesia during the 2015-2020 period has increased by 2.32% per year. The use of cooking oil repeatedly is commonplace among the people of Indonesia and quite a lot. Even though the use of cooking oil can endanger health because the frying process at high temperatures can damage the chemical structure of the oil. Therefore, in this study, image processing was carried out to identify the quality of palm oil using the Convolutional Neural Network (CNN) algorithm. This research was conducted through several stages, namely dataset collection, dataset preprocessing, CNN algorithm implementation, testing, and development of information systems. The dataset consists of image data of palm cooking oil that has not been used, palm cooking oil used for frying twice, and palm cooking oil used for frying more than twice. The total amount of data is 3000 image data. Distribution of training data and test data using the Pareto division of 80:20. Based on the test, the best accuracy is 97.08%. This research produces an android-based information system that can identify the quality of cooking oil based on the classification.
Prediction of Liver Disease Using a Linear Regression Algorithm Deny Haryadi; Dewi Marini Umi Atmaja; Arif Rahman Hakim
Journal of Informatics and Communication Technology (JICT) Vol 5 No 1
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/j_ict.v5i1.182

Abstract

The liver is the most essential organ in the human body. Hepatitis is one such disorder affecting the liver and is a global health issue, including in Indonesia. Liver disease is an inflammatory condition of the liver that can be triggered by genetic factors, viral infections, alcohol consumption, and the use of certain drugs. In principle, prevention of hepatitis or liver disease can be done by adopting a healthy lifestyle. In addition, early detection is also very important in preventing death in those affected by this disease. One method for early detection is through the application of data mining, which can help predict and reduce mortality in patients affected by this disease. Linear regression is a data mining technique utilized to predict the dependent variable or outcome based on the independent variable or predictor. The study conducted tests on this algorithm and obtained a Root Mean Squared Error of 0.349 +/- 0.000. This indicates the presence of a correlation or functional relationship (cause and effect) between the dependent variable (criterion) and the independent variable (predictor). The purpose of this testing process is to detect liver disease using the linear regression algorithm.
Identifikasi Citra Kualitas Minyak Kelapa Sawit Berbasis Android Menggunakan Algoritma Convolutional Neural Network Deny Haryadi; Sasmi Hidayatul Yulianing Tyas; Adi Kuncoro; Fiqry Firdhan Pratama Putra; Andri Ariyanto
Jurnal Rekayasa Elektrika Vol 18, No 4 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i4.28617

Abstract

The Central Statistics Agency reports that the average development of palm cooking oil consumption at the household level in Indonesia during the 2015-2020 period has increased by 2.32% per year. The use of cooking oil repeatedly is commonplace among the people of Indonesia and quite a lot. Even though the use of cooking oil can endanger health because the frying process at high temperatures can damage the chemical structure of the oil. Therefore, in this study, image processing was carried out to identify the quality of palm oil using the Convolutional Neural Network (CNN) algorithm. This research was conducted through several stages, namely dataset collection, dataset preprocessing, CNN algorithm implementation, testing, and development of information systems. The dataset consists of image data of palm cooking oil that has not been used, palm cooking oil used for frying twice, and palm cooking oil used for frying more than twice. The total amount of data is 3000 image data. Distribution of training data and test data using the Pareto division of 80:20. Based on the test, the best accuracy is 97.08%. This research produces an android-based information system that can identify the quality of cooking oil based on the classification.
Co-Authors Adi Kuncoro Amat Basri Andri Ariyanto Arif Rahman Hakim Arif Rahman Hakim Demi Adidrana Dewi Marini Umi Atmaja Fiqry Firdhan Pratama Putra Rila Mandala Risma Adisty Nilasari Sasmi Hidayatul Yulianing Tyas Seandy Arandiant Rozano Syifa Nurgaida Yutia Umi Atmaja, Dewi Marini