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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Informatika Jurnal Komputasi Jiko (Jurnal Informatika dan komputer)
Muliadi Muliadi
Lambung Mangkurat University
Computer Science & IT Education Electrical & Electronics Engineering

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Implementasi Reduksi Fitur t-SNE Pada Clustering Gambar Head shape Nematoda Muhammad Rizky Adriansyah; Mohammad Reza Faisal; Abdul Gafur; Radityo Adi Nugroho; Irwan Budiman; Muliadi Muliadi
Jurnal Komputasi Vol 10, No 1 (2022)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2963

Abstract

Pada penelitan ini dilakukan clustering terhadap gambar head shape nematoda, dalam melakukan pengolahan gambar diperlukan metode ekstraksi fitur untuk menemukan informasi penting dari gambar yang akan diolah, salah satu esktraksi fitur yang bisa digunakan adalah wavelet. Setelah gambar melewati ekstraksi fitur dihasilkan sebanyak 5624 fitur, dengan fitur sebanyak ini dapat mengakibatkan waktu komputasi yang lama. Oleh sebab itu perlu dilakukan reduksi fitur untuk mengurangi jumlah fitur yang awalnya 5624 fitur menjadi 2 atau 3 fitur saja, salah satu metode reduksi fitur terbaru yang bisa digunakan adalah t-SNE. Pada penelitian ini dilakukan perbandingan hasil kualitas cluster antara yang menggunakan reduksi fitur dengan yang tidak. Hasil Silhouette Index   yang didapatkan tanpa reduksi fitur adalah 0.046 dan setelah menggunakan reduksi fitur t-SNE terjadi peningkatan yang cukup signifikan menjadi 0.418.
IMPLEMENTASI FUZZY TSUKAMOTO DALAM PENENTUAN KESESUAIAN LAHAN UNTUK TANAMAN KARET DAN KELAPA SAWIT Maya Yusida; Dwi Kartini; Radityo Adi Nugroho; Muliadi Muliadi
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 4, No 2 (2017)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v4i2.115

Abstract

Land suitability is the suitability of a plot of land for a particular use. In the determination of appropriate plant recommendations on land, the Banjarbaru Swampland Food Crops Research Institute sets out 8 criteria in its assessment. These criteria include Soil Depth (cm), CEC Soil (cmol), Saturation Bases (%), pH (H2O), C-Organic (%), N Total (%), P2O5 (mg / 100g), K2O (mg / 100g). Making this expert system using Fuzzy Tsukamoto method. The results obtained from this expert system in the form of data on land suitability for rubber and palm oil plantations that are prioritized to be planted in a field based on the growing requirements of a plant. Keywords: Expert System, Land Suitability, Fuzzy TsukamotoKesesuaian lahan adalah kecocokan sebidang lahan untuk penggunaan tertentu. Dalam penentuan rekomendasi tanaman yang sesuai terhadap lahan, Balai Penelitian Tanaman Pangan Lahan Rawa Banjarbaru menetapkan 8 kriteria dalam penilaiannya. Kriteria tersebut meliputi Kedalaman Tanah (cm), KTK Tanah (cmol), Kejenuhan Basa (%), pH (H2O), C-Organik (%), N Total (%), P2O5 (mg/100g), K2O (mg/100g). Pembuatan sistem pakar ini menggunakan metode Fuzzy Tsukamoto. Hasil yang didapat dari sistem pakar ini berupa data tingkat kesesuaian lahan untuk tanaman karet dan kelapa sawit yang lebih diprioritaskan untuk ditanam disuatu lahan berdasarkan syarat tumbuh suatu tanaman. Kata Kunci : Sistem Pakar, Kesesuaian Lahan, Fuzzy Tsukamoto
Random Forest Dengan Random Search Terhadap Ketidakseimbangan Kelas Pada Prediksi Gagal Jantung Muhammad Ali Abubakar; Muliadi Muliadi; Andi Farmadi; Rudy Herteno; Rahmat Ramadhani
Jurnal Informatika Vol 10, No 1 (2023): April 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i1.14531

Abstract

Prediksi keberlangsungan hidup pasien gagal jantung telah dilakukan pada penelitian untuk mencari tahu tentang kinerja, akurasi, presisi dan performa dari model prediksi ataupun metode yang digunakan dalam penelitian, dengan menggunakan dataset heart failure clinical records. Namun dataset ini memiliki permasalahan yaitu bersifat tidak seimbang yang dapat menurunkan kinerja model prediksi karena cenderung menghasilkan prediksi kelas mayoritas. Pada penelitian ini menggunakan pendekatan level algoritma untuk mengatasi ketidakseimbangan kelas yaitu teknik bagging dengan metode Random Forest lalu digabungkan dengan metode Hyper-Parameter Tuning agar kinerja yang dihasilkan menjadi lebih baik. Selanjutnya model dilatih dengan dataset dan dibandingkan dengan metode lain, hasilnya menunjukkan bahwa Random Forest dengan Random Search Hyper Parameter-Tuning mencapai nilai AUC sebesar 0,906 dan untuk model Random Forest tanpa Random Search memperoleh nilai AUC sebesar 0,866. Prediction of the survival of heart failure patients has been carried out in research to find out about the performance, accuracy, precision and performance of the prediction model or method used in the study, using the heart failure clinical records dataset. However, this dataset has a problem, namely being unbalanced which can reduce the performance of the prediction model because it tends to produce predictions for the majority class. This study uses an algorithm level approach to overcome class imbalance, namely the bagging technique with the Random Forest method and then combined with the Hyper-Parameter Tuning method so that the resulting performance is better. Then the model was trained with the dataset and compared with other methods, the results showed that the Random Forest with Random Search Hyper Parameter-Tuning achieved an AUC value of 0,906 and for the Random Forest model without Random Search the AUC value of 0,866 was obtained. 
IMPLEMENTATION OF INFORMATION GAIN AND PARTICLE SWARM OPTIMIZATION UPON COVID-19 HANDLING SENTIMENT ANALYSIS BY USING K-NEAREST NEIGHBOR Riana Riana; Muhammad I Mazdadi; Irwan Budiman; Muliadi Muliadi; Rudy Herteno
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 1 (2023)
Publisher : JIKO (Jurnal Informatika dan Komputer)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i1.5260

Abstract

In early 2020, a new virus from Wuhan, China, identified as the coronavirus or COVID-19, shocked the entire world. (Coronavirus Disease 2019). The government has made various attempts to combat this outbreak, despite the fact that the government's involvement in combating Covid-19 has many benefits and disadvantages. One of the most commonly debated subjects on Twitter is the Indonesian government's response to the Covid-19 virus. This research compares the k-nearest neighbor classification technique, Information Gain feature selection with the K-Nearest Neighbor classification algorithm, and Information Gain feature selection and Particle Swarm Optimization optimization with the K-Nearest Neighbor classification algorithm. Comparisons are performed to determine which method is more accurate. Because it is frequently used for text and data categorization, the K-Nearest Neighbor algorithm was selected. The K-Nearest Neighbor algorithm has flaws, including the ability to be fooled by irrelevant characteristics and being less than ideal in finding the value of k. The selection of the Information Gain feature could indeed solve this issue by decreasing Terms that are less important and to optimize the K-Nearest Neighbor categorization, an optimization method with the Particle Swarm Optimization algorithm is employed to maximize the K-Nearest Neighbor classification. According to the results of this research, the K-Nearest Neighbor categorization with Information Gain feature selection and Particle Swarm Optimization optimization is better than the K-Nearest Neighbor model without selecting features and without optimization and is better than the K-Nearest Neighbor model with Information Gain selecting features, notably 87,33% with a value of K 5.
Comparison Algorithm for Diabetes Classification with Consideration of Mutual Information and Information Feature Rahmat Ramadhani; Triando Hamonangan Saragih; Muhammad Itqan Mazdadi; Muliadi Muliadi
Jurnal Komputasi Vol 11, No 1 (2023): Jurnal Komputasi
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v11i1.6649

Abstract

Diabetes is a prevalent disease in humans that is caused by excessive sugar levels in the body. If left untreated, it can lead to severe consequences such as paralysis, decay in certain parts of the body, and even death. Unfortunately, early detection of diabetes is difficult, and many cases go untreated until it is too late. However, the development of technology has opened up new possibilities for early detection and treatment of diabetes. One such approach is classification, a commonly used method in the field of Computer Science. Classification is used in various fields, including health, agriculture, and animal diseases, to draw conclusions based on input data using cause-and-effect relationships. Many different learning concepts and methods can be used in classification, with the Decision Tree concept being one of the most popular examples. This study compares several classification methods, including Decision Tree, Random Forest, AdaBoost, and Stochastic Gradient Boost, with feature selections carried out using MI and IF. The study aims to evaluate the effectiveness of these methods and the influence of feature selection on improving their performance. Based on the results of the study, it can be concluded that feature selection using Mutual Information and Importance Feature can improve the classification accuracy in some methods, particularly in Random Forest, AdaBoost, and Stochastic Gradient Boost. However, the Decision Tree algorithm did not show any improvement in accuracy after feature selection. The best classification accuracy was achieved with the Stochastic Gradient Boost method using the original dataset without feature selection, while the Random Forest method showed the highest accuracy after using all the features. Overall, the results suggest that feature selection can be a useful technique for improving the performance of classification algorithms in diabetes prediction. The study suggests that future research could investigate other classification methods, such as Neural Network or Deep Learning, and use optimization algorithms like Genetic Algorithm or Particle Swarm Optimization to improve feature selection results.
Application of SMOTE to Handle Imbalance Class in Deposit Classification Using the Extreme Gradient Boosting Algorithm Dina Arifah; Triando Hamonangan Saragih; Dwi Kartini; Muliadi Muliadi; Muhammad Itqan Mazdadi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26155

Abstract

Deposits became one of the main products and funding sources for banks and increasing deposit marketing is very important. However, telemarketing as a form of deposit marketing is less effective and efficient as it requires calling every customer for deposit offers. Therefore, the identification of potential deposit customers was necessary so that telemarketing became more effective and efficient by targeting the right customers, thus improving bank marketing performance with the ultimate goal of increasing sources of funding for banks. To identify customers, data mining is used with the UCI Bank Marketing Dataset from a Portuguese banking institution. This dataset consists of 45,211 records with 17 attributes. The classification algorithm used is Extreme Gradient Boosting (XGBoost) which is suitable for large data. The data used has a high-class imbalance, with "yes" and "no" percentages of 11.7% and 88.3%, respectively. Therefore, the proposed solution in the research, which focused on addressing the Imbalance Class in the Bank marketing dataset, was to use Synthetic Minority Over-sampling (SMOTE) and the XGBoost method. The result of the XGBoost study was an accuracy of 0.91016, precision of 0.79476, recall of 0.72928, F1-Score of 0.56198, ROC Area of 0.93831, and AUCPR of 0.63886. After SMOTE was applied, the accuracy was 0.91072, the precision was 0.78883, the recall was 0.75588, F1-Score was 0.59153, ROC Area was 0.93723, and AUCPR was 0.63733. The results showed that XGBoost and SMOTE could outperform other algorithms such as K-Nearest Neighbor, Random Forest, Logistic Regression, Artificial Neural Network, Naïve Bayes, and Support Vector Machine in terms of accuracy. This study contributes to the development of effective machine learning models that can be used as a support system for information technology experts in the finance and banking industries to identify potential customers interested in subscribing to deposits and increasing bank funding sources.
Comparison of Industrial Business Grouping Using Fuzzy C-Means and Fuzzy Possibilistic C-Means Methods Mega Lestari; Dwi Kartini; Irwan Budiman; Mohammad Reza Faisal; Muliadi Muliadi
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2548

Abstract

The industrial business sector plays a role in the development of the economic sector in developing countries such as Indonesia. In this case, many industrial businesses are growing, but the data has not been processed or analyzed to produce important information that can be processed into knowledge using data mining. One of the data mining techniques used in this research is data grouping, or clustering. This research was conducted to determine the comparison results of the Cluster Validity Index on Fuzzy C-Means and Fuzzy Possibilistic C-Means methods for clustering industrial businesses in Tanah Bumbu Regency. In each process, 5 trials were conducted with the number of clusters, namely 3, 4, 5, 6, and 7, and for the attributes used: Male Labor, Female Labor, Investment Value, Production Value, and BW/BP Value. Furthermore, this study will evaluate the Cluster Validity Index, namely the Partition Entropy Index, Partition Coefficient index, and Modified Partition Coefficient Index. This research provides the best performance results in the Fuzzy C-Means method with the results of the Cluster Validity Index on the Partition Entropy Index of 0.21566, Partition Coefficient Index of 0.88078, and Modified Partition Coefficient Index of 0.82117, and the best number of clusters is 3 with the labels of low competitive industry clusters, medium competitive industry clusters, and highly competitive industry clusters.
Co-Authors Abdul Gafur Andi Farmadi Dina Arifah Dwi Kartini, Dwi Irwan Budiman Irwan Budiman Maya Yusida Mega Lestari Muhammad Ali Abubakar Muhammad I Mazdadi Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muhammad Rizky Adriansyah Radityo Adi Nugroho Rahmat Ramadhani Riana Riana Rudy Herteno Saragih, Triando Hamonangan