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All Journal Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer JOURNAL OF APPLIED BUSINESS ADMINISTRATION Indonesian Journal of Electrical Engineering and Computer Science
Muhammad Tanzil Furqon
Fakultas Ilmu Komputer , Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mechanical Engineering Social Sciences

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Prediksi Harga Beras menggunakan Metode Least Square Brillian Ghulam Ash Shidiq; Muhammad Tanzil Furqon; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Basic ingredients are something that must be fulfilled in order to support the survival of all humans, one of the main ingredients is rice. Rice is a major commodity of staples needed by humans in several countries in the world. As a result, if the price of the staple food of rice experiences erratic price fluctuations (fluctuations) which can cause a reduction in the purchasing power of people with low incomes, the rice mills stop operating to avoid losses. Therefore, to avoid and reduce the risk of food security problems in the future, we need a system that can be able to predict the prices of rice so that it can help reduce the risk of loss and can ensure that rice prices can be reached by the wider community. In making this system, of course, a periodic data method (time series) is needed that can process real data in order to predict the price of rice staples correctly. In this study the method used is the Least Square method. This method can predict rice prices by using data in the past to be used as forecasting guidelines in the future. From the prediction results, the smallest error search calculation (error) is carried out using MAPE (Mean Absolute Percentage Error). The data used in this study were 132 data. Which consists of 132 training data and 12 test data the latest data. After the prediction process is carried out the result from the prediction prices of rice for 12 months in 2021 are obtained. The results of the prediction of rice prices for 12 months in 2021 are obtained. The test results from this prediction are in the form of the smallest error value (error) which is considered very good in accuracy to predict rice prices using the least square method, which is 5%.
Temu Kembali Informasi terhadap Sinopsis Film menggunakan Metode BM25F Adinda Chilliya Basuki; Muhammad Tanzil Furqon; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Films were originally live images or moving photos forcing the viewer to see continuous motion between different objects quickly and successively. The world of cinema is currently increasingly being embellished with technological advances, so that public interest in watching films is also increasing. Before launching a new film, the film industry always provides a brief description first in the form of writing how the story of the film is called a synopsis. Searching a synopsis of a film that is done manually takes a long time because of the large amount of data available on social networks. This makes it difficult for users to find the most relevant synopsis to what they are looking for. The information retrieval system for the film synopsis is used to meet the needs of users in searching for information from a document. One of the document ranking methods that can be used for information retrieval is the BM25F method. Testing was performed on BM25F independent parameters. The best value in the precision@k and r-precision tests on 300 film synopsis documents against 6 queries using the free parameter BM25F, the best combination of boost values obtained in the boost variable test is in the title field = 5 and content = 1, the best bc value from bc variable testing is 0.75, and the best k1 value from the results of k1 variable testing is 1.2. The best average value for precision@k is 0.93 and for the best results in the r-precision test, the value is 1.
Sistem Pendukung Keputusan Perbaikan Jalan menggunakan Metode Fuzzy Multi Atribute Decision Making (FMADM) dan Simple Additive Weighting (SAW) (Studi Kasus : Dinas Pekerjaan Umum Bina Marga dan Sumber Daya Air Kabupaten Jember) Guntur Syafiqi Adidarmawan; Muhammad Tanzil Furqon; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 5 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Road repairs have been planned by Dinas Pekerjaan Umum Bina Marga dan SDA Kabupaten Jember by repairing 50 roads in the Jember City area. This road repair is carried out to increase the quality of roads with good condition to 70 percent from 61.88 percent. In the process of determining which roads will be repaired, there are difficulties encountered. There are many factors that need to be considered, so that it requires accuracy in decision making. This study aims to implement the FMADM and SAW methods in a decision support system to determine the priority ranking of roads that need improvement. In addition to implementing the algorithm and knowing its accuracy, this study also aims to determine how sensitive the criteria are. Based on research results, the accuracy results were 88 percent in the first scenario and 60 percent in the second scenario, and get a coefficient value of 0.470 on the Spearman Rank Correlation test. The results of the sensitivity test using 9 criteria were 92.11 percent. From the test, the criteria for moderate road conditions are the criteria that have the highest sensitivity by producing 99 ranking changes and the criteria for lightly damaged road conditions are the criteria with the lowest sensitivity with 72 ranking changes.
Klasifikasi Berat Badan Lahir Rendah (BBLR) menggunakan Metode Support Vector Machine dengan Teknik SMOTE Anindya Celena Khansa Kirana; Muhammad Tanzil Furqon; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 7 (2022): Juli 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the main causes of infant mortality is associated with an increase in the Neonatal Mortality Rate (AKN) with low birth weight (LBW). LBW needs to be identified and predicted to prevent death when knowing the risk of LBW. In this study, a classification system was built as the initial identification of LBW. The data used comes from medical record data for childbirth at the Ardimulyo Public Health Center, Malang Regency for the January-August 2021 period in the form of imbalanced class. In this study, the method used is the Support Vector Machine (SVM) by combining the Synthetic Minority Oversampling Technique (SMOTE) technique. The performance of the SVM method without the SMOTE technique and the SVM method with the SMOTE technique using a linear kernel and RBF are compared in this study. Tests were carried out using 3-fold cross validation on kernel and parameter testing to find the best method and independent data testing of all methods. to compare the two. Based on testing the evaluation results obtained are less than optimal because they get low results. By testing the 3-fold cross validation test, the best results are obtained on the RBF kernel with the parameter lamda of 0.1, gamma of 0.001, complexity of 20, maximum iteration of 100, and epsilon of 0.001. Meanwhile, the results of data testing show that the best method is the RBF kernel SVM method without using SMOTE, which results in accuracy of 0.75, precision of 0.5, recall of 0.2, and f-measure of 0.2857.
Klasifikasi Jamur Dapat Dimakan atau Beracun Menggunakan Naive Bayes dan Seleksi Fitur berbasis Association Rule Mining Muhammad Rafif Al Aziz; Muhammad Tanzil Furqon; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 8 (2022): Agustus 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mushrooms are food commodities that are nutritious for the body. Even mushrooms can be a cure for certain diseases. But not all types of mushrooms are nutritious for the body, there are types of mushrooms that can even be bad for the body or toxic. Therefore the classification of edible and poisonous mushrooms is very important in order to be able to consume the right mushrooms. The classification method used to classify edible or poisonous mushrooms is Naive Bayes with feature selection based on Association Rule. Prior to classification using Naive Bayes, an Association Rule-based feature selection is performed by selecting features in the rule that meet minimum support and minimum confidence. The best accuracy result of mushrooms classification with Naive Bayes and feature selection is 95% with one selected feature. Meanwhile, the seven selected features produce an accuracy of 94%. If without feature selection the resulting accuracy is 95%. Although the accuracy with feature selection is not better than without feature selection, by using feature selection the computational performance of the model becomes more efficient and accuracy only decreases by 1%. This means that feature selection based on Association Rule and classification using Naive Bayes is successful in classifying mushrooms.
Penggunaan Metode Ekstraksi Fitur Tekstur Gray Level Co-ocurrrence Matrix dan K-Nearest Neighbor untuk Identifikasi Jenis Penyakit Tanaman Apel Muhammad Iqbal Mustofa; Muhammad Tanzil Furqon; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia is a country with a tropical climate that makes it easy for apple plants to grow, even the apple crop farming industry is one of the fields that is widely cultivated in Indonesia. Malang is one of the largest apple producing areas in Indonesia. It was also explained in the data from the Central Statistics Agency (BPS) in 2019 that Malang Regency could produce 1,406,173 quintals of apples. In apple cultivation, pest and disease control is one of the important factors in the development of apple plants because it can affect the yield and quality of apples. There are several main diseases that attack plants such as Apple Scab caused by the fungus Venturia inaequalis, Black Rot caused by the fungus Botryosphaeria obtusa and Cedar-Apple Rust caused by the fungus Gymnosporangium juniperi-virginianae. Information technology is needed to speed up the process of identifying apple plant diseases. This study utilizes the results of texture feature extraction of Gray Level Co-occurrence Matrix (GLCM) and the K-Nearest Neighbor classification method. In this study, the data used were 1943 leaf images with 4 classes including Apple Scab, Black Rot, Cedar Apple Rust and Healthy. The GLCM features used in this research are Variance, Homogeneity, Energy and Correlation. In the evaluation, the K-fold cross validation method was used to eliminate bias in the data with k=10. Of all the tests carried out, the highest average accuracy was 84.56% at an angle of 90° with a value of d=2 and at a value of k=3 with the Euclidean Distance calculation method.
Pengembangan Sistem Pelaporan Akuntabilitas Kinerja Instansi Pemerintah Fakultas Ilmu Komputer Universitas Brawijaya Iman Harie Nawanto; Fajar Pradana; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the efforts to realize a clean and good government is by reporting the performance accountability of government agencies. The report was also carried out at the Faculty of Computer Science, Universitas Brawijaya. The Quality Assurance Group of the Faculty of Computer Science has the responsibility for making the report. The quality assurance team had many problems when making the report. At this time for the manufacture of the report is done using Microsoft Excel with more than 40 sheets. It is not surprising that when the data processing is carried out errors occur such as entering the wrong data or accidentally deleting data. In addition, it is necessary to carry out data processing to find out that the data entered has met the achievement target or not. The current data collection is still done manually by taking data from several fields, so if there is a change in the data from these fields, it is necessary to re-collect the data. Therefore, the authors conducted research by developing an application of a government agency performance accountability system at the scope of the computer science faculty to assist the quality assurance group in preparing government agency performance reports. Website-based applications. The application development method used is the waterfall. This method is very suitable to be used when developing applications whose needs are clear. Based on the results of the needs analysis, there are 428 functional requirements and 1 non-functional requirement. This application is developed using Laravel and Bootstrap framework. In the validation test using black box, the test results are 100% valid. In usability testing using the usability scale system, the results obtained an average score of 70.625 so that it is classified as acceptable. In addition, the compatibility test results that the application can be used on a browser that meets non-functional requirements.
Klasifikasi Kelayakan Calon Kreditur menggunakan Metode Syntethic Minority Over-Sampling Technique (SMOTE) dan K-Nearest Neighbor (KNN) Muhammad Wafiq; Lailil Muflikhah; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 11 (2022): November 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In classification, one of the problems that is often encountered is the imbalance class. Unbalanced data occurs when the amount of the data from one class has more or less than the other classes. Classes with unbalanced data will cause the classification results to be skewed towards classes that have more data. Some classifiers are unable to produce maximum accuracy when used on unbalanced data. To overcome the problem of imbalance class, the Synthetic Minority Over-Sampling Technique (SMOTE) method can be used. This method will generate new synthetic data which will be used as training data for the classification process. The method used for classification in this study is the K-Nearest Neighbor (KNN) method. The accuracy value that obtained whe n the data is classified using the KNN method without using SMOTE is 60%. Meanwhile, when the unbalanced data is handled first using SMOTE method and then classified using the KNN method, the accuracy value obtained is 85%. From the test result, the best parameter values were k=1 and N=100.
Pengelompokan Film Berdasarkan Alur Cerita menggunakan Metode Self Organizing Maps dan Silhouette Coefficient Wilis Biro Syamhuri; Muhammad Tanzil Furqon; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Movie is one of the most popular entertainment media around the world by presenting interesting audio and visuals. Now movies can be easily accessed via streaming service provider sites or applications, even with mobile devices. The site or application that provides movie streaming service has one simple but very useful feature namely movie grouping, which are generally based on genre, year, rating, country of origin, and others. Each movie has its own unique plot or storyline, but there are several movies that have similar plots in some parts. Regardless of the same genre, sometimes there are movies that have different plots. Vice versa, there are movies with different genres but have similarities in plots. On the basis of these problems, this research grouped movies based on similarities in plots. Grouping is done using the Self Organizing Maps clustering method. After that, the results of the clustered movies were evaluated using the Silhouette Coefficient method. A total of 800 movies were successfully grouped into 25 clusters with the highest evaluation result of 0.175. One out of twenty-five clusters has the highest local evaluation score of 0.714. The results of this evaluation indicate that grouping movies based on plots is still not fully effective because only one out of twenty-five clusters produces an evaluation value in the "strong structure" category.
Sistem Pendukung Keputusan Penyakit Stroke menggunakan Metode Fuzzy Tsukamoto dengan Basis Pengetahuan Framingham Risk Score Arief Andy Soebroto; Muhammad Tanzil Furqon; Eko Ari Setijono Marhendraputro; Wildan Ziaulhaq
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 8, No 2 (2022): Volume 8 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v8i2.56362

Abstract

Penyakit stroke adalah salah kerusakan pada otak yang muncul secara mendadak akibat gangguan peredaran darah otak non-traumatis. Gangguan tersebut dapat berupa pembuluh darah tersumbat yang dapat menghambat atau menghentikan aliran darah ke otak. Penyakit stroke di Indonesia telah mengalami peningkatan, angka prevalensi per mil telah meningkat dari 7% pada tahun 2013 menjadi sebesar 10,9% pada tahun 2018. Penyakit stroke dapat dikurangi dengan melakukan deteksi dini pada masyarakat supaya dapat melakukan tindakan preventif. Deteksi dini penyakit stroke memiliki kondisi data yang semi terstruktur karena banyaknya faktor untuk mengidentifikasi risiko penyakit stroke. Kondisi data semi terstruktur akan mempersulit deteksi dini penyakit stroke sehingga diperlukan alat bantu berupa sistem pendukung keputusan (SPK). Penelitian dilakukan dengan membangun sistem pendukung keputusan deteksi dini penyakit stroke menggunakan metode Fuzzy Tsukamoto. Model basis pengetahuan menggunakan Framingham Risk Score sebagai dasar untuk pembuatan aturan (rule) klasifikasi dengan 120 data pasien Puskesmas Kendalkerep Kota Malang. Hasil pengujian yang didapatkan adalah akurasi sebesar 0,8444, presisi sebesar 0,7801, recall sebesar 0,796, specificity sebesar 0,8891, dan F1 score sebesar 0,751.
Co-Authors Abas Saritua Gultom Abu Wildan Mucholladin Achmad Arwan Achmad Ridok Adinda Chilliya Basuki Adinugroho, Sigit Agus Wahyu Widodo Ahmad Afif Supianto Akhmad Eriq Ghozali Al-Mar'atush Shoolihah Aldion Cahya Imanda Amalia Luhung Andini Agustina Anindya Celena Khansa Kirana Anjelika Hutapea Annisya Aprilia Prasanti Annisya Aprilia Prasanti Ardisa Tamara Putri Arief Andy Soebroto Arif Indra Kurnia Arina Rufaida Arinda Rachman Arjun Nurdiansyah Arya Perdana Arynda Kusuma Dewi Aryo Pinandito Aryu Hanifah Aji Asfie Nurjanah Audi Nuermey Hanafi Ayu Anggrestianingsih Barik Kresna Amijaya Bayu Rahayudi Bayu Rahayudi Bossarito Putro Brillian Ghulam Ash Shidiq Budi Darma Setiawan Candra Dewi Cusen Mosabeth Daniel Alex Saroha Simamora David Bernhard Defanto Hanif Yoranda Dendry Zeta Maliha Destin Eva Dila Purnama Sari Desy Andriani Diajeng Sekar Seruni Dian Eka Ratnawati Dwi Yana Wijaya Dyan Dyanmita Putri Dyang Falila Pramesti Dzar Romaita Edy Santoso Eko Ari Setijono Marhendraputro Eky Cahya Pratama Elan Putra Madani Erwin Bagus Nugroho Evilia Nur Harsanti Fadhilla Puji Cahyani Fahmi Achmad Fauzi Fajar Pradana Fatwa Ramdani, Fatwa Fernando Parulian Saputra Fikar Cevi Anggian Firdaus Rahman Fitra Abdurrachman Bachtiar Gabriel Mulyawan Ghulam Mahmudi Al Azis Guntur Syafiqi Adidarmawan Hangga Eka Febrianto Hanifa Maulani Ramadhan Hanifah Khoirunnisak Hugo Ghally Imanaka Humam Aziz Romdhoni I Gusti Ngurah Ersania Susena Imam Cholissodin Iman Harie Nawanto Imaning Dyah Larasati Inas Hakimah Kurniasih Indra Eka Mandriana Indri Monika Parapat Indriana Candra Dewi Indriati Indriati Inggang Perwangsa Nuralam Issa Arwani Jojor Jennifer BR Sianipar Julita Gandasari Ariana Jumerlyanti Mase Kevin Nadio Dwi Putra Khaira Istiqara Laila Diana Khulyati Lailil Muflikhah Listiya Surtiningsih Luthfi Faisal Rafiq M. Ali Fauzi Mahardhika Hendra Bagaskara Mahendra Data Maria Sartika Tambun Marji Marji Masayu Vidya Rosyidah Mochamad Ali Fahmi Muh. Arif Rahman Muhamad Fahrur Rozi Muhammad Aghni Nur Lazuardy Muhammad Iqbal Mustofa Muhammad Rafif Al Aziz Muhammad Riduan Indra Hariwijaya Muhammad Wafiq Naufal Sakagraha Kuspinta Nindy Deka Nivani Novanto Yudistira Nur Kholida Afkarina Nurdifa Febrianti Nurudin Santoso Nurul Hidayat Nurul Hidayat Nurul Ihsani Fadilah Ofi Eka Novyanti Oky Krisdiantoro Pangestuti, Edriana Pricielya Alviyonita Priyambadha, Bayu Putra Pandu Adikara Putri Indhira Utami Paudi R Moh Andriawan Adikara Raden Rafika Anugrahning Putri Raditya Rinandyaswara Raditya Rinandyaswara Rahman Syarif Randy Cahya Wihandika Ratna Ayu Wijayanti Restia Dwi Oktavianing Tyas Ridho Ghiffary Muhammad Rifaldi Raya Rifwan Hamidi Rimba Anditya Kurniawan Riski Nova Saputra Riza Rizqiana Perdana Putri Rizal Setya Perdana Robbiyatul Munawarah Romlah Tantiati Satrio Hadi Wijoyo Setyoko Yudho Baskoro Silvia Aprilla Sutrisno Sutrisno Tania Oka Sianturi Taufan Nugraha Teri Kincowati Tryse Rezza Biantong Ulva Febriana Vandi Cahya Rachmandika Vania Nuraini Latifah Vera Rusmalawati Vianti Mala Anggraeni Kusuma Weni Agustina Wildan Afif Abidullah Wildan Ziaulhaq Wildan Ziaulhaq Wilis Biro Syamhuri Yuita Arum Sari Yuita Arum Sari