Garuda - Garba Rujukan Digital

Business Intelligence Dashboard Visualization on Information Systems for Online Verification of Invoice Documents and Requests for Goods or Services Hadiwinata, Daad; Fauziah, F; Mardiani, Eri
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.748

In the daily running of their business, business companies are usually faced with the problem of adapting to changing trends related to the business world. To make a decision that is precise, fast, and accurate based on data and analysis, the company must apply Business Intelligence (BI) based technology. In this research, the study was conducted at PT ADHI KARYA (Persero) Tbk, which has a problem with the document verification system which is still done manually. To help the above problems, a solution is offered by utilizing Business Intelligence (BI) based technology to create a dashboard of information systems for verification of invoice documents and requests for goods and services online. For evaluating the performance of employees or verifier officers can use the collaboration between BI and the K-Means Algorithm by classifying incoming data based on the duration of the data input until the e-Verification sheet is sent by the system. The system development method in this study uses the RAD (Rapid Application Development) method of rapid application development. Which is expected to produce a solution in the form of a visualization dashboard for online verification information systems for invoice documents and requests for goods and services based on Business Intelligence technology.

Analisis Sentimen Identifikasi Opini Terhadap Produk, Layanan dan Kebijakan Perusahaan Menggunakan Algoritma TF-IDF dan SentiStrength Aziz, Abdul; Fauziah, F
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.430

The need to analyze a product or policy becomes an important thing to measure the level of success. Twitter is currently one of the popular applications used by the public to give their impressions and opinions, both positive, negative and neutral opinions. Diverse public opinion on Twitter can be used as a reference material to get the level of community satisfaction on a product, service or policy. In this study, a sentiment analysis system was created using the TF-IDF and SentiStrength Algorithm. The steps in the research are, firstly, crawling Twitter data using the Twitter API, second preprocessing, thirdly doing spell correction, fourth Word weighting (TF-IDF) and lastly SentiStrength classification, where the results of the classification of tweets have positive, negative or neutral sentiments. In the test data taken using the keyword "child vaccines" as many as 1000 tweets, the results obtained were 54% positive sentiment, 20% negative sentiment and 26% neutral sentiment. Comparison with the same negative data analysis using a different algorithm, namely Naïve Bayes, results in positive sentiment of 55%, 16% and neutral 29%. Decision Tree got 61% positive results, 14% negative and 25% neutral.

Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes Wandani, Aprilia; Fauziah, F; Andrianingsih, A
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.365

There is a sales system called Flash Sale in e-commerce. Basically the concept of a Flash Sale is to offer a lower price and a predetermined time and number of products. The sales system is only held at certain moments, by making cheaper product sales but with a limited time and number of products it will make sales increase because buyer interest will be higher. But apart from all the advantages of course there will be pros and cons. Sentiment analysis on Twitter was chosen because Twitter itself is a social media that allows users to be free to comment or write opinions about anything, including opinions about flash sale events that exist in e-commerce today. Thus, this research exists to find out the opinions of existing Twitter users regarding the Flash Sale event held by e-commerce. By using the methodology of three classification algorithms, Naive Bayes, K-Nearest Neighbor and Random Forest in classifying the data to determine the accuracy of the sentiment value of Twitter users in the Flash Sale event. This research takes two data samples from the keywords "flash sale" and "flash sale shopee", the results accuracy of the implementation of the three classification algorithms are 83.53% Naive Bayes, 82.94% K-NN, 80.59% Random Forest for the keyword "flash sale” and 81.48% Naive Bayes, 77.78% K-NN, 74.07% Random Forest for the keyword “flash sale shopee”. With this, the Naive Bayes Algorithm becomes a recommendation for classifying Sentiment Analysis data with greater accuracy and more stability to be used for large and small data.

Tempat sampah pintar berbasis sensor HC-SR04 menggunakan Aduino Uno R3 Junaed, Ismail; Fauziah, F; Nuraini, Rini
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.366

Humans can produce waste every day, both household waste and industrial waste which has various forms and types. Garbage can be a problem because it interferes with human health, causes unpleasant odors and air pollution. Awareness of disposing of waste in its place is currently considered very lacking. This is because trash cans in general still use a simple way, namely by opening and closing manually. In the design of this smart trash can it will facilitate the work of the cleaning agency. In this study, a smart trash can with HC-SR04 sensor was designed using Arduino Uno R3 With the HC-SR04 sensor, it can detect the distance of humans who want to throw away and detect garbage loads. The maximum and minimum distance from the sensor is 10-60 cm to open the lid of the trash can. Data processing uses Arduino Uno R3 with Atmega328p chip which functions as data processing and sending data. The testing process that is carried out periodically produces data in the form of differences in the original distance and the distance read by the sensor with a difference of 0-1 cm, while the minimum and maximum sensor distances are 10-60 cm. This system can also detect the garbage load if it is full and will send a notification in the form of SMS. Another benefit is to make people aware of the importance of health by disposing of waste in its place. The method used is the waterfall method to detect this object is the design method which consists of several stages, namely Requirements Analysis, Design, Circuit Implementation, Test Tools.

Analisis Sentimen Terhadap Kebijakan Pemerintah Tentang Larangan Mudik Hari Raya Idulfitri di Indonesia Tahun 2021 Menggunkan Metode Naïve Bayes Aziz, Abdul; Fauziah, F; Fitri, Iskandar
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.381

Social media as a place to access and disseminate information has grown very rapidly, one of which is Twitter. Twitter, as a place for information flow, is a rich source for seeking public opinion and sentiment analysis. Twitter in this study was used as a source to obtain data about the 2021 homecoming in Indonesia. The purpose of this study is to determine public satisfaction with government policies regarding the ban on going home in Indonesia in 2021. The data to be processed is Indonesian-language tweets, the keywords are #mudik and #diarangmudik, the length of data collection is 1 week, with lots of data generated as many as 1000. Sentiment analysis in this study using the Naïve Bayes Classification method. The steps in this study are first crawling Twitter data which is then stored in csv format, second preprocessing which consists of tokenizer, case folding, cleansing and stop removal, third Naive Bayes classification which will be carried out after going through the Pre-processing stage, where the results of the classification tweets tend to be positive or negative or neutral. The results of this study obtained an accuracy of 56.52% with each positive sentiment value of 62.28%, negative sentiment as much as 46.72% and neutral sentiment as much as 66.50%.

Analisis Sentimen Identifikasi Opini Terhadap Produk, Layanan dan Kebijakan Perusahaan Menggunakan Algoritma TF-IDF dan SentiStrength Aziz, Abdul; Fauziah, F
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.430

The need to analyze a product or policy becomes an important thing to measure the level of success. Twitter is currently one of the popular applications used by the public to give their impressions and opinions, both positive, negative and neutral opinions. Diverse public opinion on Twitter can be used as a reference material to get the level of community satisfaction on a product, service or policy. In this study, a sentiment analysis system was created using the TF-IDF and SentiStrength Algorithm. The steps in the research are, firstly, crawling Twitter data using the Twitter API, second preprocessing, thirdly doing spell correction, fourth Word weighting (TF-IDF) and lastly SentiStrength classification, where the results of the classification of tweets have positive, negative or neutral sentiments. In the test data taken using the keyword "child vaccines" as many as 1000 tweets, the results obtained were 54% positive sentiment, 20% negative sentiment and 26% neutral sentiment. Comparison with the same negative data analysis using a different algorithm, namely Naïve Bayes, results in positive sentiment of 55%, 16% and neutral 29%. Decision Tree got 61% positive results, 14% negative and 25% neutral.

Sentimen Analisis Pengguna Twitter pada Event Flash Sale Menggunakan Algoritma K-NN, Random Forest, dan Naive Bayes Wandani, Aprilia; Fauziah, F; Andrianingsih, A
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.365

There is a sales system called Flash Sale in e-commerce. Basically the concept of a Flash Sale is to offer a lower price and a predetermined time and number of products. The sales system is only held at certain moments, by making cheaper product sales but with a limited time and number of products it will make sales increase because buyer interest will be higher. But apart from all the advantages of course there will be pros and cons. Sentiment analysis on Twitter was chosen because Twitter itself is a social media that allows users to be free to comment or write opinions about anything, including opinions about flash sale events that exist in e-commerce today. Thus, this research exists to find out the opinions of existing Twitter users regarding the Flash Sale event held by e-commerce. By using the methodology of three classification algorithms, Naive Bayes, K-Nearest Neighbor and Random Forest in classifying the data to determine the accuracy of the sentiment value of Twitter users in the Flash Sale event. This research takes two data samples from the keywords "flash sale" and "flash sale shopee", the results accuracy of the implementation of the three classification algorithms are 83.53% Naive Bayes, 82.94% K-NN, 80.59% Random Forest for the keyword "flash sale” and 81.48% Naive Bayes, 77.78% K-NN, 74.07% Random Forest for the keyword “flash sale shopee”. With this, the Naive Bayes Algorithm becomes a recommendation for classifying Sentiment Analysis data with greater accuracy and more stability to be used for large and small data.

Tempat sampah pintar berbasis sensor HC-SR04 menggunakan Aduino Uno R3 Junaed, Ismail; Fauziah, F; Nuraini, Rini
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.366

Humans can produce waste every day, both household waste and industrial waste which has various forms and types. Garbage can be a problem because it interferes with human health, causes unpleasant odors and air pollution. Awareness of disposing of waste in its place is currently considered very lacking. This is because trash cans in general still use a simple way, namely by opening and closing manually. In the design of this smart trash can it will facilitate the work of the cleaning agency. In this study, a smart trash can with HC-SR04 sensor was designed using Arduino Uno R3 With the HC-SR04 sensor, it can detect the distance of humans who want to throw away and detect garbage loads. The maximum and minimum distance from the sensor is 10-60 cm to open the lid of the trash can. Data processing uses Arduino Uno R3 with Atmega328p chip which functions as data processing and sending data. The testing process that is carried out periodically produces data in the form of differences in the original distance and the distance read by the sensor with a difference of 0-1 cm, while the minimum and maximum sensor distances are 10-60 cm. This system can also detect the garbage load if it is full and will send a notification in the form of SMS. Another benefit is to make people aware of the importance of health by disposing of waste in its place. The method used is the waterfall method to detect this object is the design method which consists of several stages, namely Requirements Analysis, Design, Circuit Implementation, Test Tools.

Analisis Sentimen Terhadap Kebijakan Pemerintah Tentang Larangan Mudik Hari Raya Idulfitri di Indonesia Tahun 2021 Menggunkan Metode Naïve Bayes Aziz, Abdul; Fauziah, F; Fitri, Iskandar
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.381

Social media as a place to access and disseminate information has grown very rapidly, one of which is Twitter. Twitter, as a place for information flow, is a rich source for seeking public opinion and sentiment analysis. Twitter in this study was used as a source to obtain data about the 2021 homecoming in Indonesia. The purpose of this study is to determine public satisfaction with government policies regarding the ban on going home in Indonesia in 2021. The data to be processed is Indonesian-language tweets, the keywords are #mudik and #diarangmudik, the length of data collection is 1 week, with lots of data generated as many as 1000. Sentiment analysis in this study using the Naïve Bayes Classification method. The steps in this study are first crawling Twitter data which is then stored in csv format, second preprocessing which consists of tokenizer, case folding, cleansing and stop removal, third Naive Bayes classification which will be carried out after going through the Pre-processing stage, where the results of the classification tweets tend to be positive or negative or neutral. The results of this study obtained an accuracy of 56.52% with each positive sentiment value of 62.28%, negative sentiment as much as 46.72% and neutral sentiment as much as 66.50%.

Analisis Perbandingan Algoritma Pencarian Jenis Tanaman Hias dengan Menggunakan Penerapan Metode Perbandingan Eksponensial Fauziah, F; Gunaryati, Aris; Nurhayati, N; Farahdinna, Frenda; Kaeren, K
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 4, No 2 (2023): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v4i2.195

The process of searching data manually certainly takes a long time, therefore a system is needed that can speed up the data search process, especially ornamental plant data. In this study, two algorithms were used to compare the optimal data search process according to the type of data being collected. The data used are ornamental plant data. The two algorithms used are Knuth Morris Pratt and Boyer Moore. The process is carried out by comparing text and existing patterns based on the type of character that is carried out in this test and utilizing the exponential comparison method with a value comparison of 109.13 using the Boyer Moore algorithm and 139.19 with the Knuth Morris Pratt algorithm so that the type of Boyer Moore algorithm can be known faster than Knuth Morris Pratt for text searches related to the types of ornamental plants in the trial in this study.

Title

Found 11 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 11 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 11 Documents
Search