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All Journal Journal of Environmental Engineering and Sustainable Technology 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 Kelulusan Mahasiswa Berdasarkan Kinerja Akademik Menggunakan Metode Modified K-Nearest Neighbor (MK-NN) Imaning Dyah Larasati; Ahmad Afif Supianto; Muhammad Tanzil Furqon
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 the Brawijaya University FILKOM Informatics Engineering study program, the academic performance of students in terms of study period is still a problem. In FILKOM's academic database, there are student academic data. The data can be carried out data mining by predicting students' graduation in the 5th semester. K-NN is a good method for predicting graduation. However, there is a method that has better accuracy than K-NN has been found in other cases, that is MK-NN. Therefore, this study using M-NN method for predict students' graduation based on academic performance by testing includes testing the effect of k value, the number of training data and the composition of training data. Furthermore, comparing the accuracy produced by MK-NN and K-NN methods. The highest accuracy of testing the effect of the value of k is when k = 5, which is equal to 82%. The highest accuracy from testing the effect of number of training data and the composition of training data reached 85,25% and 84%. From the comparison of the accuracy of MK-NN and K-NN it was concluded that MK-NN produced better accuracy than K-NN.
Prediksi Harga Saham menggunakan Metode Backpropagation dengan Optimasi Ant Colony Optimization David Bernhard; Muhammad Tanzil Furqon; Muh. Arif Rahman
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

Stocks are a sign of a person's or party's investment contribution to a company or limited liability company. Movement of stock prices affects the profits and losses that will be obtained by the investor. The obstacle is stock prices can change in every minute on weekdays. It takes a method that is able to predict stock prices accurately and consistently, so that it can minimize the risk of stock investment. Besides its advantages, BPNN has shortcoming, such as slow convergence time, easy convergence to local minimum points, and poor generalization capabilities. ACO has advantages in distributed computing, positive feedback, and metaheuristic properties that can improve the weaknesses of BPNN. This study uses time series data from the stock price of Bank Rakyat Indonesia (Persero) Tbk. period 1 January 2018 until 31 December 2018. ACO serves to optimize the value combination of learning rate, momentum, and number of hidden nodes for BPNN training phase. Best combination of ACO parameter values was obtained, namely the ant cycle constant worth 0.8, the control constant of pheromone intensity worth 0.1, the visibility control constant worth 0.1, the local pheromone evaporation constant worth 0.5, global pheromone evaporation constant worth 0.1, number of ants 5, and number of iterations 7. That combination produces an average of MAPE 1.745, while BPNN only reached 3.024.
Prediksi Jumlah Kunjungan Wisatawan Mancanegara Ke Indonesia Menggunakan Metode Average-Based Fuzzy Time Series Models Teri Kincowati; Muhammad Tanzil Furqon; Bayu Rahayudi
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

Indonesia is a country that has a great diversity of cultures and natural wealth. One of Indonesia's natural wealth that is the attraction of the world is its tourist attractions. The beauty of tourist attractions in Indonesia makes Indonesia becomes a country that is often visited by foreign tourists on vacation. Tourism is one business that can increase economic growth. Tourists visit is increased sigficantly due to many factors, including competitive travel costs, promotion strategies that continue to be intensified, and many adequate travel routes. This must be balanced with adequate facilities and guaranteed security. The number of visitors that cannot be ascertained must be predictable in anticipation of sudden increases or deterioration, so that the state can determine policies towards changes in the number of visitors in the future. The method that will be used to predict in this study is the average-based fuzzy time series models and the using 216 data obtained from the official website of the Statistic Indonesia, it is data of the number of foreign tourist visits to Indonesia in the period January 1999 to December 2016. Based on the result of the study obtained MAPE value is 10,140%, that MAPE value is good to predict, because it is under 20%. So can be concluded that average-based fuzzy time series is good enough to predict the number of foreign tourists visit to Indonesia.
Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Haralick Feature Extraction dengan Naive Bayes Classifier Gabriel Mulyawan; Yuita Arum Sari; Muhammad Tanzil Furqon
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

As living things, humans need to survive. One of the basic need human's bodies require to survive is food. Foods provide nutrients that contain carbohydrates, protein, minerals, fats, and vitamins for boosting endurance. Basically, foods can be easily identified with human's eyes. But it is not like the brain-computer that require the introduction or features extraction from food objects for classification. The features extraction used are HSV Color Moment for color features and Haralick for texture features. Then, the results of the features extraction will be classified using the Naive Bayes classifier method. The data set used are based of the primary data that contains pictures and the pictures were taken by the smartphone camera consist of 276 foods images.. This research uses 2 testing processes, that are the comparison of the amount of the training data and testing data, and the testing of the used features. Based on the testing of the comparison of the amount of the training data and the training data using K-Fold Cross Validation, it showed that the best accuracy is 61,95% that using 166 training data images and 110 training data images. Then, the accuracy from the features test that was just using the HSV Color Moment feature is about 57,66%. The accuracy from test that using the Haralick feature is 36,67%. The accuracy from the combination of 2 features of the HSV Color Moment and Haralick are better than only using the texture features with the 56,33% accuracy. The image processing technique using HSV Color Moment and Haralick features extraction can be used for foods image classification using the Naive Bayes classifier method.
Prediksi Harga Bitcoin Menggunakan Metode Extreme Learning Machine (ELM) dengan Optimasi Artificial Bee Colony (ABC) Arjun Nurdiansyah; Muhammad Tanzil Furqon; Bayu Rahayudi
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

Bitcoin is the most popular cryptocurrency currently being favored as a means of investment like stocks. Its nature is not centralized or decentralized which causes the price of Bitcoin can experience inflation at any time. So we need a method to predict the price of Bitcoin accurately to make decisions in Bitcoin buying and selling transactions. The ELM method has better learning speed than other methods and a simple structure, but it has disadvantages in choosing input weights and biases randomly. To overcome these shortcomings, the ABC method is used because it also has a very simple and flexible structure. Therefore, the price of Bitcoin will be predicted using the ELM-ABC method. This research uses Bitcoin price time series data from the Indodax cryptocurrency exchange from 01 December 2017 to 31 August 2018. ABC functions to produce the most optimal input weights and biases for the ELM training stage. Furthermore, input weights, biases, and output weights will be used for ELM testing stages to obtain the prediction result prices. Then, error evaluation value calculated from the results of the Bitcoin price prediction using MAPE. The ELM-ABC parameter test results get the best combination of 12 features, 20 hidden neurons, 20 bee populations, and 5 iterations. The combination produces an average MAPE value of 1,96983% and an accuracy of 98,03017%, while ELM amounted to 2,70401% and 97,29599%.
Prediksi Harga Bekatul menggunakan Metode Fuzzy Time Series Oky Krisdiantoro; Budi Darma Setiawan; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rice bran is one of the raw materials used as a mixture of animal feed concentrates, the use of rice bran in animal feed concentrates is 10% -30% from about 10 other types of raw materials mixed. with the number of percentages used, the company needs to pay attention to the availability of rice bran and also the price that changes every month, if the price of rice bran is too expensive it will change the basic price of animal feed concentrate, so the company must be able to predict the price of rice bran in the next month so that they can supply more rice bran when the prices would increase. To predict prices, we can use the fuzzy time series method with reference to historical data from the previous months, with which we can find out the price predictions of rice bran in the following month. The results of this study are expected to be able to help animal feed concentrate companies to be able to predict the price of bran in the coming months, in this study the best predictions resulted in a MAPE value of 4.43% with a number of training data 36 and interval length 12.
Sistem Pendukung Keputusan Penerimaan Pegawai Baru menggunakan Metode AHP dan TOPSIS Muhamad Fahrur Rozi; Edy Santoso; Muhammad Tanzil Furqon
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

Employees as individuals who provide expertise for companies and organizations that require worker services in an effort to achieve the needs of human resources as a plan and efforts to complete the needs of the workforce so that competent selection to get the quality and progress of the company. In this study aims to provide employee selection recommendations using the implementation of Analytic Hierarchy Proces (AHP) and Technique for Order Preference by Similarity (TOPSIS) methods which will consider the criteria used as employee selection at PT Jagaraga Adika, Surabaya by using four criteria as considerations for new recruitment selection, including initial selection, psychologist test, security training, and interview. The results of the implementation of the AHP-TOPSIS method on employee recommendations are in the form of ranking the best employees obtained from the assessment criteria used, where the data used amounted to 638 data from January to December. The system that has been built shows the best accuracy results reaching 100% in the February and June period, while the lowest results obtained by the system are 82.50% in March and with an average best accuracy reaches 91.23%.
Klasifikasi Varietas Unggul Padi menggunakan Algoritme C4.5 Arinda Rachman; Muhammad Tanzil Furqon; Fatwa Ramdani
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

Superior varieties are important supporting component in increasing national rice production. Each type of superior variety has different characteristics. Farmers need knowledge to be able to choose the type of rice they want while keeping rice production stable. To overcome the problem in determining rice seeds, one solution is to use data mining classification. The classification are using the decision tree method, with the C4.5 algorithm. C4.5 algorithm is an algorithm that builds decision trees from data with recursive patterns. C4.5 goes to each node of the decision tree, then selects the optimal branch until no more branches are generated. the algorithm can produce a classification of superior rice varieties needed to be planted in the fields. This research has input data in the form of flag leaves, grain shape, grain color, rice texture and texture. The input data is then processed by the C4.5 algorithm to produce outcomes that determine superior varieties of rice. The results of this study indicate that the classification performance of superior rice varieties is more influenced by the grain form attributes compared to other attributes. Meanwhile, the accuracy of the highest C4.5 algorithm in the k-fold 1 calculation was 28.571%. Meanwhile, k-fold 2 was 17.857%, k-fold 3 was 10.344% and k-fold 4 was 10.714%.
Implementasi Metode Learning Vector Quantization (LVQ) untuk Klasifikasi Persalinan Romlah Tantiati; Muhammad Tanzil Furqon; Candra Dewi
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

One reference to measure health services in an area is how medical care is handled by medical personnel. In this case the Maternal Mortality Rate (MMR) as well as in infants is the Infant Mortality Rate (IMR) considered as an important indicator in health care. Antenatal care services are carried out as an effort to prevent complications during pregnancy and expectant mothers by determining the actions that must be given to pregnant women from the examination results so that they are able to suppress the Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR). LVQ network is a competitive training with each output connected in a certain class. In this study the authors implemented LVQ learning to classify normal childbirth into 2 classes, namely whether childbirth is normal or at risk. By using the data collected in the Nursing Care (ASKEP) data on the general physical examination of pregnant women which contains information on age, pelvic size, fetal position, measurement of blood pressure, hemoglobin cell level (HB), results of psychology testing for prospective mothers, Upper Arm Circumference (LILA), proteinurea and Fetal Weight Interpretation (TBJ). The results of LVQ testing for the classification of normal childbirth with learning rate parameters (α) = 0.1, reduction constants LR (c) = 0.1, minimum LR = 10-7 and maxEpoch / iterations maximum 24 times with a comparison of the amount of training data and test data (64:16) is an accuracy value of 93,78%.
Klasifikasi Tweet Berbahasa Indonesia Berisi Ujaran Kebencian Menggunakan Metode Improved K-Nearest Neighbor dengan Pembobotan BM25F Nurdifa Febrianti; Indriati Indriati; Muhammad Tanzil Furqon
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

Hate speech is a verbal hatred act that targets a group of people or parts of a particular community. In Indonesia, hate speech is increasingly found, especially on text-based social media such as Twitter. So that inspired the writing of this research, to identify hate speech on Twitter with the classification of tweets, especially those in Indonesian. The author chooses to use Improved K-Nearest Neighbor by using the BM25F term weighting, which is a weighting that considers the fields/streams in the document. So the tweet chosen as a training document and research test document, consists of 2 streams, the tweet and the hashtag. K-Fold Cross Validation testing (with K = 5) was performed on the parameter k for IKNN classification, bs, vs, and k1 for BM25F weighting, with 400 training documents and 100 test documents. The test results show that the determination of stream weight values ​​on BM25F sufficiently influences the results of the IKNN classification. Meanwhile the best final results for the F-Measure, Accuracy, Precision, and Recall of the average 5-Fold Cross Validation obtained were 79.77%, 68.80%, 68.80%, and 89.92% with k = 70, bs= 0,6, v1 = 2, v2= 5 and k1= 2 as the best value for each parameter.
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 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