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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) Jurnal Pendidikan Vokasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Natural A Jurnal POINTER Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan Jurnal Teknik Otomotif Kajian Keilmuan dan Pengajaran Jurnal Preventia Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Multimedia Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian, Pendidikan dan Teknologi Jurnal Graha Pengabdian Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Abdimas Indonesian Journal Prosiding Seminar Kesehatan Nasional Sexophone National Journal of Occupational Health and Safety
Marji Marji
Fakultas Ilmu Komputer, Universitas Brawijaya
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Implementasi TOPSIS pada Sistem Rekomendasi Tempat Pembelian Sayuran Organik di Malang Berbasis Lokasi Usman Adi Nugroho; Ratih Kartika Dewi; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In Malang City, organic vegetables can be found in various places such as supermarkets, organic vegetable stores, and farmer groups. Prices and types of vegetables differ from one place to another. Due to many places that sell organic vegetables, people are confused to determine organic vegetable stores. To solve this matter, the researcher built a recommendation system for organic vegetable shop in Malang based on location. The system is built in the form of an android application because many people have used an android device, and an android device has a GPS (Global Positioning System) sensor that can be used to find out the location of an android user. In determining the place of recommendation, the system uses the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method with four criteria, namely, distance, price, variety of vegetables, and rating. Based on black box testing, the result shows that the system has 100% valid functionality. In testing algorithm validation, a matching between the manual calculation of the TOPSIS method and the results of the system has been done, and the results are the system 100% compatible with manual calculations. The last test is rank consistency by comparing the recommendations from 5 alternative and the recommendation from 6 alternative. The results of rank consistency testing show that the implementation of the TOPSIS method on the recommendation system for organic vegetable shop in Malang has consistent results.
Penerapan Metode K-Means untuk Evaluasi Senyawa Anti Kanker Berdasarkan Kajian Proteomik Sel MCF7 Mochammad Pratama Viadi; Marji Marji; Sri Widyarti
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cancer is a disease that originates from several body cells that can divide without stopping and can spread to surrounding tissues. Then split cell will attack and damage the surrounding tissue. Especially in breast cancer or occupying the top level in cancer. This cancer originates from a glandular tract in the breast which declared a malignant tumor. In the detection this disease can use bioinformatics techniques, namely proteomic studies. Output produced is a grouping of the results of the anti-cancer process on the development of cancer in the form of clustering using the K-Means process which is divided into 2, active and inactive. The results of system testing were carried out using Silhoutte Coefficient and Precision Recall Accuracy. In the Silhoutte Coefficient test getting the optimal cluster is 5 with a value of 0.6827. Whereas in testing the Precision Recall Accuracy, the Precision value was 67.59%, the Recall value was 79.57%, and the accuracy rate was at 57.71%. Using 10-fold cross validation get an average accuracy of 62.26%.
Implementasi Metode TOPSIS pada Sistem Rekomendasi Tempat Wisata Belanja di Kota Malang Berbasis Lokasi Muhammad Robby Dharmawan; Ratih Kartika Dewi; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The tourism sector is an industry that is favored by countries in the world. In 2017 foreign tourist visits coming to Indonesia amounted to 14 million visits which increased by 20% compared to 2016 which reached 11 million visits. In traveling, tourists tend to do shopping so that shopping tourism locations appear. Malang City is a city located in East Java province which has a variety of various tourist locations to be visited including shopping attractions. The number of shopping attractions in the city of Malang often makes tourists confused to have a shopping destination. At present, information about shopping places in Malang is still using the web which cannot provide recommendations to tourists. This study offers a recommendation system for shopping tourism in Malang that uses the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method because the best alternative results are alternative ranking which has the closest distance from the distance of the positive ideal solution and farthest distance is the ideal negative solution by using criteria such as rating, number of reviews, operational time, and distance. Functional testing on the system results in 100% validity. Non-functional testing on the system uses algorithm validation and usability. From the algorithm validation test, the results of the system output comparison with manual calculations have a validity of 100%. From usability testing by distributing 30 questionnaires with 25 questions, the end user satisfaction rate was 88.47%.
Implementasi TOPSIS Pada Sistem Rekomendasi Tempat Wisata Dalam Kota Malang Berbasis Lokasi Ricky Irfandi; Ratih Kartika Dewi; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Malang is the second-largest city in East Java, which is known as the tourism city because it has many interesting tourist destinations. However, many choices of tourist destinations in Malang confuse the tourists about choosing suitable tourism according to their wishes because of the limited funds and time. To overcome this problem, a mobile-based application is needed for decision-making that can provide tourist recommendations, so that tourists are expected to get travel recommendations that match their desired criteria from anywhere and anytime. The system developed based on Android so it can be used by a wide range of users. The system was built using the TOPSIS method to rank tourist recommendations according to the criteria entered by users based on tourist data provided by web services. The system has successfully fulfilled the functional requirements with a valid value of 100% on functional testing. Besides, the validation testing of algorithms which is done by comparing the results of recommendations with manual calculations and the calculation of the system gets a 100% match. And for accuracy testing by experts gets an accuracy value of 83.3%.
Analisis Sentimen Review Shopee Berbahasa Indonesia Menggunakan Improved K-Nearest Neighbor dan Jaro Winkler Distance Liana Shanty Wato Wele Keaan; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The recent development in technology has allowed technology to give ease the lives of the general public. One of the conveniences of the development is an online shopping system. Shopee is one of the available online shopping platforms. Shopee's application has provided convenience in terms of buying and selling one's products online thru a smartphone where all of those features can be accessed. In online shopping activities, the price of sold products is equally important to its quality where it is mostly shown thru reviews. Unfortunately, many consumers have difficulty in understanding certain reviews from other consumers rooting from the usage of non-standard language. Therefore, this research focuses on sentiment analysis research of reviews in the form of text which will be divided into two classes, which are positive and negative. The analysis process is started by preprocessing, word weighting thru TF-IDF, followed by normalization, and cosine similarity using the Improved K-Nearest Neighbor and Jaro Winkler Distance to repair words that are not in the standard language. Based on testing of the value of k which is acquired thru evaluation using 5-fold, the optimal value is k=10, after word repairs were done, were a value of 0,876 for accuracy, precision value are 0,810, a recall score of 0,942, and f-measure score of 0,882. Based on the testing results, the accuracy values were fluctuate which were affected by the value of k-values.
Analisis Sentimen Tentang Opini Performa Klub Sepak Bola Pada Dokumen Twitter Menggunakan Support Vector Machine Dengan Perbaikan Kata Tidak Baku Swandy Raja Manaek Pakpahan; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Football is one of the most popular sports in the world, including in Indonesia. A football club is very dependent on its supporters so that the satisfaction of supporters of a football club must be maintained. Supporters of football clubs themselves often provide arguments to a football club via Twitter media. Therefore, the authors propose research to build a sentiment analysis system for football club performance opinions on Twitter documents. This research uses the Support Vector Machine method and Levenshtein Distance for non-standard word correction. The process starts with preprocessing the data, then do word correction with Levenshtein Distance, weighting using Term Frequency-Inverse Document Frequency, followed by classification using Support Vector Machine. The test results with the highest accuracy were obtained at 83.25% with learning rate = 0,0001, complexity = 0,001, lambda = 0,1, epsilon = 0,0001 and maximum iteration = 50.
Klasifikasi Berat Badan Lahir Rendah Pada Bayi Dengan Fuzzy K-Nearest Neighbor Muhammad Rizkan Arif; Budi Darma Setiawan; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The number of infant mortality (IMR) is a measure of the success of health services in an area. The lower the IMR, the better the health services in the area. However, in 2015, the IMR value in Indonesia was very far from the agreed target as an indicator of the success of health service development. In 2013, there was an increase in LBW cases during the 2009-2013 period to 16% according to data from WHO and UNICEF. If viewed from the cause of death, low birth weight babies still rank high. As many as 2.79% of infants died from LBW in East Java in 2010. This percentage increased to 3.32% in 2013 so that LBW was classified as the main cause of neonatal death, which was 38.03% of the total birth rate. The existence of an early detection system is likely that LBW is expected to be able to help reduce infant mortality. One method that can be applied to predict the possibility of LBW is Fuzzy K-Nearest Neighbor (FK-NN). This method is proven to be able to carry out LBW classification with an accuracy rate of 79%.
Pencarian Terjemahan Hadits Shahih Muslim Menggunakan Metode Cosine Similarity Dengan Seleksi Fitur Term Frequency Achmad Burhannudin; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Muslims use the Hadith as their legal basis, so the hadith can also be a way of life for all humans, especially for Muslims. Even though the hadith books are very large and thick, one of them is the Saheeh Muslim Hadits. In this modern era, most people prefer something that is efficient and easy, and computer science has been able to extract books into data that is easier to process, making it easier for people to keep reading, learning hadith, and also can be used to solve problems. without having to carry a large book. In this case, the researcher wants to apply a computer science namely Text Mining, using the Cosine similarity method as the Algorithm in the search is supported by the selection of the Term frequency feature and the researcher wants to know how effective the method is in dealing with this search problem. After the research was conducted, the researcher got the maximum results from the specified query with a precision value of p @ 10 = 90%, p @ 20 = 54%, and p @ 30 = 42%.
Komparasi Metode Data Mining Support Vector Machine dengan Naive Bayes untuk Klasifikasi Status Kualitas Air Ricky Marten Sahalatua Tumangger; Nurul Hidayat; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Water is a chemical compound that is very important for every living thing for survival on this earth. On earth water has a very large area compared to the mainland this compound has an area of ​​almost 71% around the land. The water also consists of sea water, rivers, lakes, ground water, swamp water, snow and steam which are in the air layer that contains mineral substances that are recruited in water. To determine the classification of water quality status using the Support Vector Machine and Naive Bayes methods. This method was chosen because previous studies get high accuracy results for classification. The parameters used are the degree of acidity (pH), TDS, NO2, NO3, hardness, chloride, manganese. The Vector Machine and Naive Bayes Suppord method will provide the results of the comparison of the accuracy of the two methods. Testing on the system is done using the K-Fold Cross Valadation test with the highest accuracy results when K = 9 for the Support Vector Machine method and K = 5 for the Naive Bayes method. Testing parameters for the Support Vector Machine method gets the highest accuracy when the threshold value is , C = 3, γ = 0.01, λ = 2.5, maximum iteration value = 1, σ = 0.1. From these tests the accuracy of the Support Vector Machine method was 78.70% and the Naive Bayes method was 85.78%. The best results obtained by the classification of water quality status are the Naive Bayes method compared to using the Support Vector Machine method because the average accuracy of the Naive Bayes method is higher. Water is a chemical compound that is very important for every living thing for survival on this earth. On earth water has a very large area compared to the mainland this compound has an area of ​​almost 71% around the land. The water also consists of sea water, rivers, lakes, ground water, swamp water, snow and steam which are in the air layer that contains mineral substances that are recruited in water. To determine the classification of water quality status using the Support Vector Machine and Naive Bayes methods. This method was chosen because previous studies get high accuracy results for classification. The parameters used are the degree of acidity (pH), TDS, NO2, NO3, hardness, chloride, manganese. The Vector Machine and Naive Bayes Suppord method will provide the results of the comparison of the accuracy of the two methods. Testing on the system is done using the K-Fold Cross Valadation test with the highest accuracy results when K = 9 for the Support Vector Machine method and K = 5 for the Naive Bayes method. Testing parameters for the Support Vector Machine method gets the highest accuracy when the threshold value is , C = 3, γ = 0.01, λ = 2.5, maximum iteration value = 1, σ = 0.1. From these tests the accuracy of the Support Vector Machine method was 78.70% and the Naive Bayes method was 85.78%. The best results obtained by the classification of water quality status are the Naive Bayes method compared to using the Support Vector Machine method because the average accuracy of the Naive Bayes method is higher.
Analisis Sentimen Kepuasan Pengguna Pada Ulasan Aplikasi Marketplace Menggunakan Metode BM25F dan Neighbor-Weighted K-Nearest Neighbor Sabrina Hanifah; Indriati Indriati; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mobile applications are increasing in use, because many applications are offering convenience for their users. Users who already use the application have the right to review their experiences during application usage. These reviews are useful for new users and application developers. But there are no features in an app store that can classify these reviews into positive or negative categories. These problems can be solved by an automatic process that can analyze the reviews according to positive and negative reviews. The method used for ranking documents is BM25F and as a classification method the Neighbor-Weighted K-Nearest Neighbor (NWKNN) method is used. Testing done using K-fold Cross Validation method to determine the best number of k and confusion matrix for testing each parameter of BM25F and NWKNN. Based on testing conducted on each parameter the BM25F and NWKNN methods can produce a percentage of f-measure and accuracy reaches 97% and 96%. This proves that the NWKNN method can classify the dataset with an unequal number of classes.
Co-Authors Achmad Burhannudin Adam Hendra Brata Adhikari, Basanta Prasad Adhiyatma Mugiprakoso Afifah, Nadiyah Hanun Agi Putra Kharisma Agung Kurniawan Agustian, Moch. Alfredo Barta Ahmad Fauzan Rahman Ahmad, Baihaqi Aldy Satria Andika Harlan Andini Agustina Anita Sulistyorini Annisa Selma Zakia Ardhimas Ilham Bagus Pranata Arifin, Maulana Muhamad Asti Melani Astari Atika Anggraeni Audi Nuermey Hanafi Bagus Abdan Aziz Fahriansyah Bahruddin El Hayat Baihaq, Firda Barlian, Salwa Isna Bayu Rahayudi Bayu Septyo Adi Budi Darma Setiawan Budi Darma Stiawan Cahyo Adi Prasojo Candra Ardiansyah Choirul Anam Cindy Puspita Sari Cindy Rizki Amalya Dani Irawan Daud, Nathan Dea Widya Hutami Dewi Yanti Liliana Dian Eka R Dian Eka Ratnawati Djoko Kustono Dwi Yana Wijaya Dyva Agna Fauzan Edy Santoso Edy Santoso Edy Santoso Endang Wahyu Handamari Erwin Komara Mindarta Fanani, Erianto Fatih Kamala Nurika Gilang Ramadhan Gustian Ri'pi Hadi, Moch. Sholihul Handoyo, Samingun Hary Suswanto Hasan Ismail Ilham Romadhona Imam Cholisoddin Imam Cholissodin Imam Muda Nauri Imran Imran Indriati Indriati Indriati Indriati Issa Arwani Istiana Rachmi Istiqomah, Mutiara Titian Januar Dwi Amanda Jeffrey Simanjuntak Kenty Wantri A Kohei Arai Kurnianingtyas, Diva Lailatul Fitriah Lailil Muflikah Lailil Muflikhah Lailil Muflikkah Laily Putri Rizby Laksono Trisnantoro Leni Istikomah Liana Shanty Wato Wele Keaan Lilik Zuhriyah Lilis Damayanti Luthfi Faisal Rafiq M Chandra Cahyo Utomo M. Alfian Mizar Made Bela Pramesthi Putri Mahmudi, Wayan Firdaus Maududi, Affan Al Michael Adrian Halomoan Mochammad Pratama Viadi Mountaz, Lotu Muchammad Harly Muhamad Altof Muhamad Hilmi Hibatullah Muhammad Fakhri Mubarak Muhammad Hafidzullah Muhammad Indra Harjunada Muhammad Ramanda Hasibuan Muhammad Rizkan Arif Muhammad Robby Dharmawan Muhammad Tanzil Furqon Muhammad, Naufalsyah Falah Muzdalifah Yully Ayu Nonny Aji Sunaryo Nurul Hidaya Nurul Hidayat Nurul Hidayat Okvio Akbar Karuniawan P. P. S, Gladis Viona Pangestu, Wiyan Dwi Panji Prasuci Saputra Paryono Permadani , Anda Permatasari, Adelia Pratitha Vidya Sakta Prawidiastri, Firnadila Pricielya Alviyonita Rafely Chandra Rizkilillah Ratih Kartika Dewi Ratna Candra Ika Razaq, Hilal Nurfadhilah Retiana Fadma Pertiwi Sinaga Revinda Bertananda Riana Nurmalasari Ricky Irfandi Ricky Marten Sahalatua Tumangger Rizqi Addin Arfiansyah Rosalinda, Nadia Ryan Mahaputra Krishnanda Sabrina Hanifah Sari, Resti Novita Shinta Anggun Larasati Sri Wahyuni Sri Widyarti Sumarli Sumarli, Sumarli Supraptoa Supraptoa Supriyadi Supriyadi Sutrisno Sutrisno Swandy Raja Manaek Pakpahan Syarif Suhartadi Tahtri Nadia Utami Tawang Wulandari Tika Dwi Tama Usman Adi Nugroho Wayan Firdaus Mahmudy Wulandadi, Retno Yamlikho Karma Yayuk Wiwin Nur Fitriya Yuita Arum Sari Yusufrakadhinata, Muhammad Zulianur Khaqiqiyah