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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi CESS (Journal of Computer Engineering, System and Science) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Teknoinfo Riau Journal of Empowerment Jurnal Informatika Kaputama (JIK) Jusikom: Jurnal Sistem Informasi Ilmu Komputer JATI (Jurnal Mahasiswa Teknik Informatika) JTIK (Jurnal Teknik Informatika Kaputama) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Anugerah: Jurnal Pengabdian Kepada Masyarakat Bidang Keguruan dan Ilmu Pendidikan INCODING: Journal of Informatics and Computer Science Engineering Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Rizki Muliono
Universitas Medan Area
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

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Analisis Fast Fourier Tansform untuk Pengenalan Voice Register Wanita dalam Teknik Bernyanyi Muhathir, Muhathir; Susilawati, Susilawati; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 2, No 2 (2019): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (408.71 KB) | DOI: 10.31289/jite.v2i2.2166

Abstract

Automatic speech recognition merupakan kemampuan untuk menerima dan mengidentifikasi kata-kata yang diucapkan dengan mengubah sinyal analog ke digital, dan mengekstraksi karakteristik vokal unik seperti pitch, frekuensi, nada dan irama untuk membentuk model speaker atau sampel suara. Sampel suara yang digunakan yaitu voice register, voice register adalah pembagian wilayah suara manusia berdasarkan sumber suara, sensasi ruang resonansi, bentuk, warna, timbre suara, dan tinggi rendahnya nada yang dihasilkan. Fast Fourier Transform digunakan sebagai transformasi untuk mengolah sample suara yang akan diklasifikasi. FFT dalam mentransormasikan sinyal voice register hanya mampu mengklasifikasikan dengan rata-rata true positive rate 65.4%. 
Implementasi Algoritma Apriori Pada Data Benchmark Kosarak Dan Mushrooms muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 1, No 1 (2017): Edisi Juli
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (932.364 KB) | DOI: 10.31289/jite.v1i1.572

Abstract

Algoritma apriori saat ini lebih banyak digunakan untuk mencari frequent itemsets dan mencari aturan asosiasi untuk menemukan knowledge. Proses mencari frequent itemsets pada data secara berulang-ulang yang ada didalam database dan diakhiri ketika kandidat itemsets sampai K+1 tidak ada lagi. Algorima Apriori menggunakan secara umum menggunakan banyak jumlah memori dan waktu eksekusi dalam menemukan kombinasi dan perbandingan frequent itemsets. Hasil yang di dapatkan dengan menggunakan algoritma apriori bisa di katakan akurat saat menseleksi kombinasi itemset yang ada pada dataset sesuai dengan nilai support dan confidens nya. Untuk mengetahui seberapa akurat dan berapa jumlah sumberdaya yang di gunakan serta bagaimana perilaku algoritma apriori terhadap dataset dengan jumlah kolom data yang berbeda, maka implementasi agoritma apriori di ujikan dengan data benchmark kosarak.dat dan mushrooms.dat dengan nilai minimum support yang sama. Kedua data sets tersebut memiliki format yang berbeda pada jumlah kolom datanya yaitu data pada semua baris memiliki jumlah kolom karakter data, pada datasets kosarak.datmemiliki kolom karakter dengan panjang berbeda-beda pada setiap barisnya sedangkan pada datasets mushrooms.dat memiliki kolom karakter sebanyak 23 karakter data, artinya datasets tersebut memiliki model blok data linear atau sama. Hasil dari implementasi algoritma apriori terhadap kedua datasets tersebut didapatkan perilaku proses pada apriori yang ditampilkan dari hasil waktu eksekusi dan memori yang dipakai bahwa datasets kosarak lebih sedikit menggunakan waktu dibandingkan dengan datasets mushrooms namun penggunaan memori lebih boros, semakin kecil nilai minimum support semakin banyak komparasi kandidat yang dicari. Kata Kunci : apriori; datamining; implementasi; kosarak; mushrooms
Analisis Fast Fourier Tansform untuk Pengenalan Voice Register Wanita dalam Teknik Bernyanyi Muhathir Muhathir; Susilawati Susilawati; Rizki Muliono
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 2, No 2 (2019): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v2i2.2166

Abstract

Automatic speech recognition merupakan kemampuan untuk menerima dan mengidentifikasi kata-kata yang diucapkan dengan mengubah sinyal analog ke digital, dan mengekstraksi karakteristik vokal unik seperti pitch, frekuensi, nada dan irama untuk membentuk model speaker atau sampel suara. Sampel suara yang digunakan yaitu voice register, voice register adalah pembagian wilayah suara manusia berdasarkan sumber suara, sensasi ruang resonansi, bentuk, warna, timbre suara, dan tinggi rendahnya nada yang dihasilkan. Fast Fourier Transform digunakan sebagai transformasi untuk mengolah sample suara yang akan diklasifikasi. FFT dalam mentransormasikan sinyal voice register hanya mampu mengklasifikasikan dengan rata-rata true positive rate 65.4%. 
Identification of Pneumonia using The K-Nearest Neighbors Method using HOG Fitur Feature Extraction Nurul Khairina; Theofil Tri Saputra Sibarani; Rizki Muliono; Zulfikar Sembiring; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6216

Abstract

Pneumonia is a wet lung disease. Pneumonia is generally caused by viruses, bacteria or fungi. Not infrequently Pneumonia can cause death. The K-Nearest Neighbors method is a classification method that uses the majority value from the closest k value category. At this time people are not too worried about pneumonia because this pneumonia has symptoms like a normal cough. However, fast and accurate information from health experts is also very necessary so that pneumonia symptoms can be recognized early and how to deal with them can also be done faster. In this study, researchers will diagnose pneumonia to obtain information quickly about the symptoms of pneumonia. This information will adopt human knowledge into computers designed to solve the problem of identifying pneumonia. In this study, the K-Nearest Neighbors method will be combined with the HOG Extraction Feature to identify pneumonia more accurately. The KNN classification used is Fine KNN, Cosine KNN, and Cubic KNN. Where will be seen how the value of accuracy, precision, recall, and fi-score. The results showed that the classification could run well on the Fine KKN, Cosine KNN, and Cubic KNN methods. Fine KNN has an accuracy rate of 80.67, Cosine KNN has an accuracy rate of 84,93333, and Cubic KNN has an accuracy rate of 83,13333. Fine KNN has precision, recall and f1-score values of 0.794842, 0.923706, and 0.854442. Cosine KNN has precision, recall and f1-score values of 0.803048, 0.954039, and 0.872056. Cubic KNN has precision, recall and f1-score values of 0.73388, 0.964561, and 0.833555. From the test results, positive and negative identification of pneumonia was found to be more accurate with the Cosine KNN classification which reached 84,93333.
Image Classification of Autism Spectrum Disorder Children Using Naïve Bayes Method With Hog Feature Extraction Muhathir Muhathir; Rizki Muliono; Merri Hafni
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6365

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder that affects a person's ability to communicate and interact socially. Every year, the number of people diagnosed with Autism Spectrum Disorder rises, necessitating early detection in order to limit the number of people affected and provide proper treatment. As a result, a system was developed in this study to detect Autism Spectrum Disorder in facial photos utilizing versions of the Nave Bayes approach and HoG feature extraction. HoG feature extraction is a local intensity gradient distribution or edge direction perpendicular to the gradient direction without influencing the geometric and photometric transformations, and Nave Bayes is a method that classifies images based on probability. The experimental results of three types of naive Bayes, Bernoulli naive Bayes is the most reliable than Multinomial naive Bayes and Gaussian Naive Bayes. Accuracy, Precision, Recall, and the highest F1-Score using this method, with each value of 89.72%; 90.54%; 89.72%; and 89.9%. The next best performing Gaussian Naive Bayes, the most laborious results were obtained using Naive Bayes multinomials, which had Accuracy, Precision, Recall, and F1-Score of 65.91% each; 68.09%; 65.91%, and 64.19%.
Analysis Naïve Bayes In Classifying Fruit by Utilizing Hog Feature Extraction Muhathir Muhathir; Muhammad Hamdani Santoso; Rizki Muliono
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 1 (2020): ---> EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (300.115 KB) | DOI: 10.31289/jite.v4i1.3860

Abstract

Indonesia has abundant natural resources, especially the results of its plantations. Lots of local fruit that can be used starting from the root to the skin of the fruit. Local fruit can be consumed as fresh fruit and can also be processed into drinks and food. This is reflected in the diversity of tropical fruits found in Indonesia. Fruits that are rich in benefits and can be used as medicines such as Apples, Avocados, Apricots, and Bananas. These fruits are often found around us. In Indonesia these fruits are produced and also exported abroad. However, the limited methods and technology used to classify this fruit are interesting things to discuss and become the main focus in this research. This study analyzed using the Naïve Bayes algorithm and feature extraction of HOG (Oriented Gradient Histogram) to obtain more effective classification results. The results showed that the collection of fruit using the Naïve Bayes method and HOG feature extraction had not yet obtained maximum classification results, only with an accuracy of 56.52%.Keywords – Apple, Avocado, Apricot, Banana, Naïve Bayes, HOG.
Implementasi Algoritma Apriori Pada Data Benchmark Kosarak Dan Mushrooms Rizki muliono
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 1, No 1 (2017): Edisi Juli
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v1i1.572

Abstract

Algoritma apriori saat ini lebih banyak digunakan untuk mencari frequent itemsets dan mencari aturan asosiasi untuk menemukan knowledge. Proses mencari frequent itemsets pada data secara berulang-ulang yang ada didalam database dan diakhiri ketika kandidat itemsets sampai K+1 tidak ada lagi. Algorima Apriori menggunakan secara umum menggunakan banyak jumlah memori dan waktu eksekusi dalam menemukan kombinasi dan perbandingan frequent itemsets. Hasil yang di dapatkan dengan menggunakan algoritma apriori bisa di katakan akurat saat menseleksi kombinasi itemset yang ada pada dataset sesuai dengan nilai support dan confidens nya. Untuk mengetahui seberapa akurat dan berapa jumlah sumberdaya yang di gunakan serta bagaimana perilaku algoritma apriori terhadap dataset dengan jumlah kolom data yang berbeda, maka implementasi agoritma apriori di ujikan dengan data benchmark kosarak.dat dan mushrooms.dat dengan nilai minimum support yang sama. Kedua data sets tersebut memiliki format yang berbeda pada jumlah kolom datanya yaitu data pada semua baris memiliki jumlah kolom karakter data, pada datasets kosarak.datmemiliki kolom karakter dengan panjang berbeda-beda pada setiap barisnya sedangkan pada datasets mushrooms.dat memiliki kolom karakter sebanyak 23 karakter data, artinya datasets tersebut memiliki model blok data linear atau sama. Hasil dari implementasi algoritma apriori terhadap kedua datasets tersebut didapatkan perilaku proses pada apriori yang ditampilkan dari hasil waktu eksekusi dan memori yang dipakai bahwa datasets kosarak lebih sedikit menggunakan waktu dibandingkan dengan datasets mushrooms namun penggunaan memori lebih boros, semakin kecil nilai minimum support semakin banyak komparasi kandidat yang dicari. Kata Kunci : apriori; datamining; implementasi; kosarak; mushrooms
Sentiment Towards Social Media Politeness Ambassadors: A Case Study Using the Naive Bayes Method Fikri, Ridho Ahmad; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14404

Abstract

Social media has had a significant impact on modern society, serving as a primary platform for sharing information and opinions. One intriguing phenomenon is the viral case of a female police officer, Putri Cikita, who earned the title "Ambassador of Courtesy" due to her actions in a video. This study aims to analyze public sentiment regarding this case on Twitter using the Naive Bayes Classifier (NBC) method. The research adopts a quantitative descriptive approach with sentiment analysis based on Text Mining, utilizing Python and Google Colab. The dataset consists of 2,000 Indonesian-language tweets collected from August to November 2024 using the keywords "Ambassador of Courtesy" and "Putri Cikita." The research stages include data collection, data preprocessing (case folding, tokenizing, filtering, stemming), and sentiment labeling into positive, negative, and neutral classes. The analysis results reveal that 11.55% of tweets express positive sentiment, 68.40% are neutral, and 20.05% are negative. The Naive Bayes method proves effective in classifying textual sentiment data. This research provides insights into public perceptions of viral events and underscores the importance of public image management in the digital era.
Classification of Hepatitis Disease Using The Fuzzy Mamdani Method Hidayani, Nurul; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14426

Abstract

In the modern world, almost everyone uses technology and information. This is evident in various fields, ranging from education and employment to entertainment. Society is too dependent on technology and information, to the point of neglecting their own health. There are many diseases caused by neglecting one's own health, one of which is hepatitis. This is because some people pay little attention and are reluctant to get it checked. Because hepatitis is very dangerous for human survival, treatment must begin as soon as the first symptoms appear and assist in the early diagnosis of hepatitis. This will allow for the identification of the type of hepatitis disease. The aim of this research is to apply the Mamdani fuzzy method for the classification of hepatitis diseases. The Mamdani fuzzy method has been successfully utilized in systems for diagnosing hepatitis diseases. In this system, it will provide instructions, namely to select which symptoms are experienced, then you can choose those symptoms by checking them off, and this system will provide a diagnosis based on the symptoms experienced. The diagnosis results include the type of hepatitis disease experienced, as well as treatment solutions. The results obtained for diagnosing hepatitis A disease using fuzzy Mamdani calculation shows that 68% , and the diagnosis of hepatitis B disease using fuzzy mamdani calculations shows 53% , and the diagnosis of hepatitis C disease using fuzzy mamdani calculations shows 59%.
Coffee Quality Classification Based on Customer Reviews Using C4.5 Algorithm Siahaan, Ricardo Fransdoli; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14427

Abstract

Coffee is a very popular commodity throughout the world, and its quality is oken evaluated through customer reviews. This research aims to classify coffee quality based on reviews given by consumers using the C4.5 algorithm. C4.5 is a machine learning algorithm used to generate decision trees, which allows decision making based on relevant attributes. In this research, the data used consists of customer reviews taken from e-commerce plaVorms and coffee discussion forums. The data is then processed with natural language processing (NLP) techniques to extract important features such as sentiment, keywords and term frequency. These features are used as input for the C4.5 algorithm, which builds a classification model based on patterns contained in the data. The results of the research show that the C4.5 model is able to classify coffee quality with high accuracy, reaching up to 85%. The factors that most influence quality classification include taste, aroma, and packaging, which are frequently mentioned in reviews. In addition, the analysis also shows significant differences in the quality of coffee produced from different coffee producing regions, which can provide insight for producers to improve their products.
Co-Authors -, Muhathir Akbar Siregar, Muhammad Alwi, Buchori Bunaya Arthavia Sitorus Darkani, M. Farhan Dedi Sahputra Effiati Juliana Hasibuan Esrayanti Simanjuntak Fikri, Ridho Ahmad Harahap , Ryanda Fadillah Hasibuan, Samsul Abdul Rahman Sidik Hasudungan, Andre Hidayah, Safrina Ida Royani Irfansyah Iswandi Iswandi Juanda Hakim Lubis Juanda Hakim Lubis Laia, Aldi Irfan Marischa Elveny, Marischa Marpaung, Febriady Merri Hafni Muhammad Farhan Darkani Muhammad Hamdani Santoso Muhathir Muhathir Muhathir Muhathir, Muhathir Muhathir, Muhathir Nanda Novita, Nanda Napisah, Napisah Nurul Hidayani Nurul Khairina Nurul Khairina Prasetyo , Dewo Putri, Riza Dwi Razaq, Faisal Sandy, Boy Siahaan, Ricardo Fransdoli Siregar, Yusril Izza Haholongan Sitopu, Essay Puspita Sri Juwita Sri Wahyuni Suhaira, Zatin Supriatno, Supriatno Susilawati Susilawati Susilawati Susilawati, Susilawati Theofil Tri Saputra Sibarani Tino Hermanto Y. Syah, Rahmad B. Yopan Rahmad Aldori Zebua, Meniati Zulfikar Sembiring Zulfikar Sembirirng