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Product Recommendation System Application Using Web-Based Equivalence Class Transformation (Eclat) algorithm Gito Resmi, Mochzen; Hermanto, Teguh Iman; Ghozali, Miftah Al
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11454

The use of saved transaction data can provide a lot of knowledge that useful to the company in making policy and find the strategy in Alfamidi. In applying that goal, that is using Market Business Analysis. One of the techniques of Data Mining is Association Rule, which is the procedure of Market Basket Analysis to find the customer buying patterns. This pattern can be one of the ways in making policy and business strategy. One pattern determined by two parameters, they are support (support value) and confidence (certainly value). This analysis used algorithm Equivalence Class Transformation (ECLAT). One of the patterns resulted from analysis to the 30 transaction data with 12 category items. As an instance, if we buy strawberry jam then buy essence of bread with confidence value = 1%. The results obtained an also be used in helping the Alfamidi to help in determine the inventory decisions. So, the conclusion may be taken if consumers could buy strawberry jam then bought essence of bread simultaneously, then the Alfamidi should at least maintain the availability stock of both these items in order to remain the same.

UI/UX Design for Language Learning Mobile Application Chob Learn Thai Using the Design Thinking Method Krishnavarty, Ayumas Aura; Defriani, Meriska; Hermanto, Teguh Iman
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11585

Thai language is one of the most difficult languages to learn because the Thai language itself has a variety of consonants, vowels, and tones to determine vocabulary. The problem is people currently have in learning Thai is the lack of knowledge about each consonant, vowels, or tones. So that it makes some people who want to learn Thai feel confused. Therefore, a Thai language learning application design was made which aims to make it easier for people who want to learn Thai language and of course it is more practical because it is in a mobile form that can be accessed anywhere and anytime easily. Design thinking is a method known as a comprehensive thinking process that aims to create a solution. In design thinking are have five stages, namely Empathize, Define, Ideate, Prototype and Test. At the test stage, the method used is Single Ease Question. The Single Ease Question has seven Likert scales where for a value range of 4 – 5.9 it is included in the interpretation quite easily, and in the range of 6 – 6.9 the interpretation is easy and for a value of 7, the interpretation is very easy. The result obtained after testing the prototype to the respondents the value obtained is 6.6 with a minimum value of 6 and a maximum value of 7. Thus, the result of 6.6 are included in the category of being easy to use by users.

Sentiment Analysis Of Tourist Reviews Using K-Nearest Neighbors Algorithm And Support Vector Machine Sari, Anita Wulan; Hermanto, Teguh Iman; Defriani, Meriska
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12447

After Indonesia was awarded as a country with extraordinary natural charm, many foreign tourists came to Indonesia. According to the records of the Central Bureau of Statistics for 2020, approximately 5.47 million foreign tourists entered Indonesia. With the large number of foreign tourist visits, the need for tourist attractions is increasing, but finding information is now not difficult. One source of information for finding reviews of tourist attractions is TripAdvisor. On this website, there is a lot of information or reviews about various tourist attractions. However, the number of reviews makes tourists confused about identifying the quality of tourist attractions to be visited, so sentiment analysis needs to be done. Sentiment analysis itself is a technique to extract, identify, and understand sentiments or opinions contained in a text. In this research, two classification methods will be used in sentiment analysis techniques, namely K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). Besides that, the object of this research will be to focus on the most popular tourist attractions in Indonesia according to Trip Advisor, namely Waterbom Bali, Mandala Suci Wenara Wana, Teras Sawah Tegalalang, Pura Tanah Lot, and Pura Luhur Uluwatu. The purpose of the research is to find out the results of accurate sentiment analysis for the five tourist attractions and compare the two algorithms used. and after testing, it was found that the Support Vector Machine algorithm is superior to the K-Nearest Neighbors algorithm.

UI/UX Analysis of Project Management Information System (PMIS) Website Using User-Centered Design Method Azhar, Sarah Afifah; Defriani, Meriska; Hermanto, Teguh Iman
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12725

In Industry 4.0, a lot of data has been digitized and is no longer stored manually. In helping to record all the projects that were worked on initially stored in outstanding Project Management applications such as Trello. However, the Trello application has limitations and compatibility with business processes, including employees not writing down projects that have been done because they are not in accordance with procedures, causing many projects to go unrecorded. So because of these limitations a Project Management Information System (PMIS) website is needed which makes it easy to record all projects so that there is no more unrecorded data. Furthermore, there are problems in conducting an analysis that can make it easier for users and how to design a User Interface and User Experience on the PMIS website using the User Centered Design (UCD) method, which in the manufacturing process will continue to make changes according to needs. The results of the User Interface design that has been made will be tested for User Experience using the Single Ease Questionnaire (SEQ) method as a measure of the success of the User Interface that has been made. Based on the results of the User Experience test using the Single Ease Questionnaire method for 5 respondents, an average value of 6.3 was obtained, which means that it can be concluded that the User Interface that has been created has a level of convenience that is in accordance with the User Experience.

Sentiment Analysis of Mobile Provider Application Reviews Using Naive Bayes Algorithm and Support Vector Machine Ningsih, Tiara Sari; Hermanto, Teguh Iman; Nugroho, Imam Ma'ruf
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13469

To choose a mobile provider to use, prospective users often rely on reviews left by previous users of the mobile provider application. One source of information for finding reviews of cellular provider applications is the Google Play Store. The purpose of this research is to analyze user reviews of cellular provider applications and find out the comparison of the accuracy levels of the two algorithms to be used, namely the Naïve Bayes Classification (NBC) and Support Vector Machine (SVM) algorithms. The object of this research is focused on the three most popular applications in Indonesia, according to the Goodstate website, namely Telkomsel, IM3, and XL Axiata. After testing using the Naïve Bayes Clasification method, the accuracy value obtained in the MyTelkomsel application is 75%, MyIM3 is 80%, and MyXL is 72%. While the Support Vector Machine method obtained an accuracy value of 77% for MyTelkomsel, 80% for MyIM3, and 76% for MyXL.

KLASIFIKASI JENIS PENYAKIT PADA DAUN TUMBUHAN STROBERI MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK ARSITEKTUR INCEPTIONV3 Sitohang, Andrian Herbert Parsaoran; Hermanto, Teguh Iman; Lestari, Candra Dewi
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5274

Stroberi adalah salah satu komoditas tanaman dengan nilai ekonomi yang cukup tinggi di Indonesia. Namun, terdapat upaya yang dapat mengurangi kualitas dan kuantitas hasil panen stroberi, yaitu penyakit-penyakit daun stroberi, termasuk leaf scorch. Oleh karena itu, deteksi dini dan klasifikasi penyakit mengenai daun stroberi ini sangat penting untuk melakukan tindakan yang diperlukan agar kerugian dapat diminimalkan. Tujuan dari penelitian ini adalah untuk mengembangkan model klasifikasi penyakit pada daun stroberi dengan metode Convolutional Neural Network menggunakan arsitektur InceptionV3. Proses penelitian yang dibutuhkan dalam pengembangan model melibatkan pengumpulan dataset gambar daun stroberi yang terdiri dari kategori sehat dan penyakit leaf scorch. Model CNN dilatih dan diuji dengan menggunakan dataset gambar daun tersebut untuk mengidentifikasi dan mengklasifikasikan jenis penyakit dengan tingkat akurasi yang tinggi. Hasil dari penelitian ini menunjukkan bahwa model CNN dengan arsitektur InceptionV3 berhasil mencapai akurasi klasifikasi sebesar 99%, menandakan keandalan model dalam mendeteksi penyakit pada daun stroberi. Selain itu, Implementasi model ini dalam aplikasi berbasis Android diharapkan dapat memberikan alat bantu yang praktis dan efisien bagi petani dalam memantau dan mengendalikan penyakit tanaman secara real-time, sehingga dapat meningkatkan kualitas dan kuantitas hasil panen stroberi. Dengan demikian, penelitian ini tidak hanya berkontribusi pada pengembangan teknologi deteksi penyakit tanaman tetapi juga pada peningkatan produktivitas pertanian.

Analisis Marketplace Shopee Untuk Memprediksi Penjualan dengan Algoritma Regresi Linier Syakir, Yusuf; Hermanto, Teguh Iman; Ramadhan, Yudhi Raymond
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.501

Many methods can be used to predict sales, one of which is the processing of sales data using the method of data mining with a linear regression algorithm. The data in this study used is data on sales of the Ariqa Collection Boutique in the Shopee marketplace starting from May 2020 to April 2022. By using a linear regression algorithm, the Ariqa Collection Boutique can predict sales estimates based on total visitors and total orders. The data mining method used is SEMMA (Sample, Explore, Modify, Model, Assess). With the Rapidminer Studio 9.10 tools the test results Mean Square Error (MSE) value is 5.172.628.212.404, Root Mean Square Error (RMSE) is 2.274.341, and Mean Absolute Percentage Error (MAPE) is 4.34%. Based on the MAPE value obtained, the accuracy of the linear regression algorithm in predicting sales of Ariqa Collection Boutique in the Shopee marketplace provides high accuracy

Analisis Sentimen Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization Terhadap Pelaksanaan Program Merdeka Belajar Kampus Merdeka Undamayanti, Erina; Hermanto, Teguh Iman; Kaniawulan, Ismi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.502

During the MBKM program running at several universities in Indonesia, several problems occurred, namely the implementation of the curriculum that did not have a reference, the disbursement of pocket money given was not on schedule, the policies of each partner were different, and the existence of the covid-19 pandemic. The way to find out public opinion or opinion about the MBKM program is to summarize public opinion on Twitter social media. This study aims to analyze the results of the classification of twitter users opinions on the MBKM program in Indonesia through sentiment analysis using the Naive Bayes method based on Particle Swarm Optimization. The research metodology carried out in this study was through the stages of data crawling, text preprocessing, feature extraction, classification, and evaluation. The data used in this study are 428 data. The results of the research in the form of sentiment analysis obtained are positive sentiments of 61.92%, it can be concluded that the MBKM program can be well received by the Twitter user community, especially students. Although there are some negative sentiments that appear around 38.08%. The results of this study can be used as a reference for the MBKM policy development team, especially the Kemendikbud POKJA team, because this program can provide benefits and experiences for students while the results of this research can be used as evaluation material for the team in the future to be even better

Analisis Sebaran Titik Rawan Bencana dengan K-Means Clustering dalam Penanganan Bencana Hermanto, Teguh Iman; Muhyidin, Yusuf
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.332

Puwakata Regency has fertile land, agricultural products and abundant natural resources. However, the area is also vulnerable to disaster risk. Based on the data collected, the disasters that occurred in Puwakata Regency included several categories, namely landslides, droughts, hurricanes and floods. The trend of increasing numbers of disasters requires further investigation to prevent an increase in the number of victims. Given the large amount of data available, this information can be obtained through data mining analysis methods. For natural disaster data, the clustering method in data mining is very useful for grouping disaster data based on the same characteristics, so that it can be used as a basis for classifying future disaster events. The k-means algorithm is a model used to form clusters by measuring how close it is to the data set. Therefore, in terms of the location of the disaster, the type of disaster and its impact on the disaster, it is hoped that this research can use the clustering technique with the k-means algorithm to classify disaster-prone points. The results obtained 3 clusters, namely, the type of drought disaster is cluster 0, the type of landslide is cluster 1, and the type of landslide is cluster 2. After forming three clusters, disaster management strategies are drawn up at each disaster-prone point in the Purwakarta area

Analisis Marketplace Shopee Untuk Memprediksi Penjualan dengan Algoritma Regresi Linier Syakir, Yusuf; Hermanto, Teguh Iman; Ramadhan, Yudhi Raymond
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.501

Many methods can be used to predict sales, one of which is the processing of sales data using the method of data mining with a linear regression algorithm. The data in this study used is data on sales of the Ariqa Collection Boutique in the Shopee marketplace starting from May 2020 to April 2022. By using a linear regression algorithm, the Ariqa Collection Boutique can predict sales estimates based on total visitors and total orders. The data mining method used is SEMMA (Sample, Explore, Modify, Model, Assess). With the Rapidminer Studio 9.10 tools the test results Mean Square Error (MSE) value is 5.172.628.212.404, Root Mean Square Error (RMSE) is 2.274.341, and Mean Absolute Percentage Error (MAPE) is 4.34%. Based on the MAPE value obtained, the accuracy of the linear regression algorithm in predicting sales of Ariqa Collection Boutique in the Shopee marketplace provides high accuracy

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