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All Journal Jurnal Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Compiler Jurnal Teknologi Informasi dan Terapan (J-TIT)
Taufiq Rizaldi
Politeknik Negeri Jember
Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science

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PERBANDINGAN METODE CBR, NN-5 DAN NAÏVE BAYES UNTUK KLASIFIKASI INFEKSI NOSOKOMIAL Taufiq Rizaldi; Aji Seto Arifianto
Jurnal Teknologi Informasi dan Terapan Vol 1 No 2 (2014)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

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Abstract

Classification method is a method that is widely used for the completion of the diagnosis of disease, particularly in humans. One type of disease that is dangerous is potentially emerging nosocomial infection in patients postoperatively. Approach classification method based upon the similarity of character data, there is also a reference to the emergence of statistical data. Some methods of classification needs to be tested in order to obtain the appropriate reference method for this case. The purpose of this study was to compare the performance of three methods of classification that Case Based Reasoning method (CBR), Neirest Neighbor-5 (NN-5) and Naïve Bayes for diagnosis of nosocomial infection. Test results showed that the method of CBR and NN-5 has a very good degree of accuracy than Naïve Bayes, Naïve Bayes but has a faster processing time.
PERBANDINGAN METODE WEB SCRAPING MENGGUNAKAN CSS SELECTOR DAN XPATH SELECTOR Taufiq Rizaldi; Hermawan Arief
Jurnal Ilmu Komputer Vol 10 No 2 (2017): September 2017
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (754.571 KB) | DOI: 10.24843/jik.2017.v10.i02.p05

Abstract

Pemanfaatan data atau berita yang tersebar di internet untuk meningkatkan peluang keberhasilan dalam sebuah usaha melalui analisa trend pasar adalah hal yang sangat umum pada saat ini. Penjelajahan Web (Crawl) dan ekstraksi data dari web (Scraping) menjadi salah satu hal yang penting, agar tidak terjadi data yang kurang sempurna, dan data yang diterima adalah data yang paling baru. CSS Selector dan Xpath merupakan salah satu metode yang umum digunakan dalam melakukan proses crawling. Terdapat perbedaan dari jumlah data yang terambil, besar file output dan waktu pemrosesan dari kedua metode tersebut, dimana Xpath memiliki keunggulan pada jumlah data yang terambil dan waktu pemrosesnya yang berakibat pada ukuran file output yang lebih besar. Sedangkan untuk penggunaan memory pada kedua metode pada proses crawling tidak memiliki perbedaan yang signifikan.
Nitrogen (N) Fertilizer Measuring Instrument On Maize-Based Plant Microcontroller Hendra Yufit Riskiawan; Taufiq Rizaldi; Dwi Putro S. Setyohadi; Tri Leksono
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (574.166 KB) | DOI: 10.11591/eecsi.v4.985

Abstract

One of the growth factors of corn plant is fertilizer according to nitrogen fertilizer requirement. The identification of nitrogen fertilizer requirement in corn plant can be done by measuring the green leaf level using Color Leaf Manual, using TCS3200 color sensor combined with Arduino Uno Board microcontroller, and information. In this study a tool was created that could automatically measure the amount of fertilizer needed for corn per hectare. The results of the measurements displayed on the LCD 2x16 bits Micro made a measurement  of  fertilizer  based  on  leaf  color  for  corn plants. By taking  the RGB value  from the leaf  that comes through the color sensor and then compared with the RGB value in the leaf color chart that has been saved in microcontroller  will  get  the  information  of  the  fertilizer dosage needed. The level of truth of the measuring instrument of fertilizer can be categorized good enough with the level of accuracy reached 82%.
Classification of Twitter User Sentiments Against Government Policies in Overcoming Covid-19 in Indonesia Hermawan Arief Putranto; Taufiq Rizaldi; Wahyu Kurnia Dewanto; Rokhimatus Zahro
Compiler Vol 11, No 2 (2022): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (834.289 KB) | DOI: 10.28989/compiler.v11i2.1286

Abstract

Sentiment classification is a field of study that analyzes a person's opinions, sentiments, judgments, evaluations, attitudes, and emotions regarding a particular topic, service, product, individual, organization, or activity. The topic that is currently being discussed is Covid-19. Covid19 is a disease caused by the corona virus, which was first identified in the city of Wuhan, China. This disease has spread throughout the world, including Indonesia. In this regard, the Indonesian government issued a policy as an effort to break the chain of the spread of the corona virus. However, this encourages the emergence of various kinds of public responses. One of them is Twitter users, there are pros and cons responses from the community in responding to government policies and causing problems, namely the difficulty of knowing positive, neutral or negative responses given by the community. Based on the explanation above, a sentiment analysis was carried out. This analysis was carried out by utilizing data from Twitter with the keywords at home, vaccines for the people of Indonesia, and PSBB, covid, covid19, covid Indonesia, vaccines Jakarta, vaccines, vaccines Restore RI, and vaccines for the sake of protecting the Republic of Indonesia. Where the data will be processed through several stages, namely preprocessing, word weighting and sentiment analysis. The results of the classification of the sentiment classification of the majority of Twitter users are neutral, namely 69.2% of the data classified as neutral sentiment, 30.1% of the data classified as positive sentiment, and 7% of the data having negative sentiment.
Arc Circularitie Naive Bayes for Occupational Safety Helmet Detection Taufiq Rizaldi; Hermawan Arief Putranto
Compiler Vol 12, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v12i2.1760

Abstract

Occupational Safety and Health (OHS) is an effort to guarantee and protect the safety and health of every worker through efforts to prevent work accidents and work-related diseases. Safety Helmet is one of the components that must exist and be used in accordance with Occupational Safety and Health standards. Detection of safety helmets usage is one of the efforts to support these activities. The application of Arc Circularity Naive Bayes is used to detect whether an object meets the ratio of a circle by utilizing RGB and HSV image filtering and classification using Naïve Bayes. That method is used to detect whether a worker uses a safety helmet or not, it also detects helm color. The average value of accuracy is 50.8, precision is 58.3%, recall is 66.0%, and f1-score is 59.5% which are calculated using the Confusion Matrix
Co-Authors Aji Seto Arifianto Dwi Putro S. Setyohadi Hendra Yufit Riskiawan Hermawan Arief Hermawan Arief Putranto Rokhimatus Zahro Tri Leksono Wahyu Kurnia Dewanto