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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 Harga Emas Dengan Menggunakan Metode Average-Based Fuzzy Time Series Muhammad Riduan Indra Hariwijaya; Muhammad Tanzil Furqon; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 4 (2020): April 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Gold is a type of precious metal that has economic value and is often used for investmentl. Demand for gold increases from year to year, because many people already know that gold can be used as a safe haven. Safe haven is ownership in the form of investment assets that have a risk in low level, so that it becomes a protector of assets. Behind the benefits of gold, many investors discourage their investment because they are afraid of being cheated and cannot predict the increase or decrease in gold prices. Therefore we need a prediction of gold prices for investors to avoid losses when they want to invest in gold. One prediction method is Average-Based Fuzzy Time Series with the advantage to determine the interval effectively, the interval formed implements Average-based length so that it can increase the accuracy of the resulting prediction. The Average-Based Fuzzy Time Series implements fuzzy logic principles for the process of making predictions, such as fuzzy set, degree of membership, fuzzification, and defuzzification. The data used are daily gold prices taken from the official website of Logam Mulia with 2700 data with a time span from January 2010 to December 2019. The best error value MAPE obtained in the study was 0.34216% and included in the very criteria good because it's under 10%. Based on research conducted, the Average-Based Fuzzy Time Series method is good for predicting gold prices.
Prediksi Penjualan Hijab menggunakan Metode Extreme Learning Machine (ELM) (Studi Kasus: Vie Hijab Store) Kevin Nadio Dwi Putra; Muhammad Tanzil Furqon; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 6 (2020): Juni 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In the industrial world nowadays, many people have established any business from micro to macro. One of the business that put focus of this research is a home industry selling hijab named Vie Hijab Store. Vie Hijab Store has increased sales year by year, but there are problems in supply management of raw materials in the form of fabrics. Therefore, by predicting the amount of hijab sales, it is expected to be able to assist the owner with building consideration in making the decision to purchase raw material in a certain period. This research will use Extreme Learning Machine (ELM) prediction method which has advantages in learning speed and for calculating the error rate of the predicted results using Mean Average Percentage Error (MAPE). The smallest MAPE results obtained for the Khimar model were 22% and for the Pashmina model by 12% using 4 features, 5 hidden nodes, a binary sigmoid activation function, and a data ratio of 60%: 40% for the Khimar model and 70%: 30% for the Pashmina model.
Prediksi Pertumbuhan Jumlah Penduduk Kota Malang menggunakan Metode Average-based Fuzzy Time Series Pricielya Alviyonita; Muhammad Tanzil Furqon; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 7 (2020): Juli 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The population growth in Indonesia continues to increase every year, making Indonesia as the 4th country with the largest population in the world. High population growth will greatly affect several aspects, such as the economy, the quality of people's lives, health and development planning. Prediction of population growth is needed to anticipate the negative affects of population growth which is expected to assist the government in making future urban planning plans. The Population and Civil Registry Office also needs these predictions to make a draft budget of needs such as e-KTP blanks, birth certificates, and others. Average-based fuzzy time series is one method of forecasting the results of the development of fuzzy time series. The fuzzy time series method based on average is able to determine the length of the effective interval so that it can produce predictions with a low error rate. By using time series data, the population of Malang City per month with a total of 123 data, this study implements the average-based fuzzy times series method to predict population growth. Based on testing in this study it can be concluded that the amount of training data has an influence on the value of MAPE produced, but the use of training data that is increasingly not always in line with the lower error value and the lowest Mean Absolute Percentage Error (MAPE) value of 0.02810%.
Peramalan Indeks Harga Konsumen Indonesia menggunakan Metode Jaringan Saraf Tiruan Backpropagation Elan Putra Madani; Muhammad Tanzil Furqon; Nurul Hidayat
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 9 (2020): September 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the focus of the government in the 2020 macroeconomic strategy is the realization of controlled inflation, an indicator often used to measure inflation, namely the Consumer Price Index (CPI). The movement of prices of goods and services consumed by the public causes changes in the value of the CPI, when unstable price movements can cause inflation. Forecasting is used to help policy makers to be taken into consideration in order to avoid inflation instability. This research used IHK as data inputs which will be formed the pattern then did data normalization process and processed using Backpropagation Neural Network method for the forecasting of CPI, return value with data denormalization and lastly using Mean Absolute Percentage Error (MAPE) for evaluation of forecasting results. The smallest MAPE value obtained from this research is 0.463% with the value of neuron input = 6, hidden neuron value = 10, initial weight range value in the range -1 to 1, learning rate value = 0.1, and epoch value = 5000
Pencarian Teks Pada Terjemahan Hadits Shahih Bukhari Dengan Metode WIDF dan Bray-Curtis Distance Luthfi Faisal Rafiq; Muhammad Tanzil Furqon; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 12 (2020): Desember 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

With the development of the application technology for searching the Hadith translation text in digital form, it is one of the good learning media to help the Hadith learning process. There have been many types of digital Hadith that have been spread. However, most of the Hadith translation text searches still use keyword-based search technology, which will result in some related Hadith translation texts not being displayed or irrelevant Hadith translation texts being displayed. To solve this problem, this study uses a term weighting algorithm and distance measurement to maximize the search results for the Hadith text. Several stages in this research are the text preprocessing stage which produces terms to represent documents, the term Weighted Inverse Document Frequency (WIDF) weighting method to provide weight values ​​for each term and the Bray-Curtis distance measurement to determine the similarity between the query and the document. In the testing process used 5 different queries and Mean Average Precision (MAP). The results of testing the effect of using term weighting, the term weighting with the best performance is WIDF weighting with a MAP value of 0.71 when the number of documents returned is 5. For the test results of the distance measurement algorithm, the best distance measurement algorithm is Bray-Curtis Distance with a MAP value of 0.71 while in Euclidean the MAP value is 0.2 on the same number of returned documents. Meanwhile, the results of the test evaluation showed that the best variation in the value of n on the number of n documents returned was 5 with a MAP value of 0.632. The greater the n value, the greater the diversity of documents, as a result, the greater the chance for irrelevant documents to be taken which causes the MAP value to decrease.
Optimasi Rute Distribusi Lokal Buah Segar Menggunakan Algoritme Genetika (Studi Kasus: PT Great Giant Pineapple) Asfie Nurjanah; Agus Wahyu Widodo; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 12 (2020): Desember 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In terms of time and financial costs, transportation and distribution are the biggest challenges for the fresh produce industry in Indonesia. On time delivery and the freshness of the product when it reaches the customer are demands that must be met by the company, as experienced by PT Great Giant Pineapple. To solve this problem, a system is needed to improve the quality of distributing products by minimizing distribution distances by finding the optimal route for the vehicle by considering travel time, service time and vehicle transport capacity. This problem in determining the optimal distribution route is known as the Vehicle Routing Problem with Time Windows (VRPTW). Genetic algorithm is one method that can be used to solve the VRPTW problem. The genetic algorithm uses a permutation representation where the chromosome length represents the number of subscribers. The search for solutions is carried out by combining chromosomes and then processed using genetic operators (mutation, crossover, and selection) by initializing genetic operators (population size, number of generations, crossover probability, and mutation probability). The test results show that the distribution route optimization using a genetic algorithm can reduce the travel time in distributing goods by 1 hour 37 minutes with the highest fitness value obtained at population size 120, the number of generations 100, the combination of crossover probability value 0.4 and mutation probability 0.6.
Klasifikasi Body Shaming Berbahasa Indonesia pada Komentar Instagram dengan Pembobotan TF-IDF-C menggunakan Metode Modified K-Nearest Neighbor (MK-NN) Audi Nuermey Hanafi; Muhammad Tanzil Furqon; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Body shaming is the act of denouncing one's physical appearance or even oneself. Some of the negative effects of body shaming include the victim feeling embarrassed, lack of confidence, and depression. The most terrible impact that can be caused by body shaming is suicide. Body shaming is often found on social media, especially Instagram. The classification system does a body shaming great job of classifying Instagram comments regarding body shaming more efficiently. This classification system body shaming has several stages. The first stage is data collection in the form of Instagram comments. This study collected 230 comments data. The second stage is text processing. Furthermore, the third stage is word weighting using the TF-IDF-C method. After obtaining the weight of each word, then the classification stage uses the method Modified K-Nearest Neighbor (MK-NN). The final result in the form of category classification of test data would enter the category of body shaming or not the body shaming. Based on the test results, the best average accuracy value is 0.530 or 53%. This proves that the TF-IDF-C weighting method and the MK-NN classification method produces the highest level of accuracy when using 60 features and K = 1. However, the level of accuracy is not good in classifying body shaming in Instagram comments.
Klasifikasi Penyakit Diabetes menggunakan Metode Support Vector Machine Abu Wildan Mucholladin; Fitra Abdurrachman Bachtiar; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Diabetes mellitus (DM) is a chronic disease associated with high levels of sugar or glucose in the blood. Diabetes is caused by one of two causes, autoimmune reactions (the body's defense system attacks insulin-producing cells) or insulin resistance (the body does not fully respond to insulin). The purpose of this research is to create a machine learning model that can detect diabetes early. There are many ways to diagnose diabetes, one of the methods is using machine learning. Support Vector Machine (SVM) is a machine learning method that is known to be quite effective for classification cases. The dataset is cleaned and normalized before so it can be ready to input in the SVM model. The SVM model is processed and tested in order to find the best model for making a diagnosis. The output of the SVM model will diagnose patients who suffer diabetes or not. The SVM model is divided into two types, the benchmark model which is implemented using the Sequential Minimal Optimization (SMO) algorithm and the scratch model which is implemented using the Sequential Learning algorithm. Each model is optimized using the Grid Search algorithm so that it can find optimal hyperparameters that can be used by the model. The optimal model is retested on several metrics using 10-fold cross validation. The test results show that the benchmark model has 0,87 mean accuracy, 0,82 mean precision, 0,78 mean sensitivity, and 0,92 mean specificity. The scratch model has 0,78 mean accuracy, 0,69 mean precision, 0,59 mean sensitivity, dan 0,87 mean specificity. The experimental results show that the Support Vector Machine method has the potential to be used as an early detection tool for diabetes.
Klasifikasi Jenis Tanaman Tembakau di Indonesia menggunakan Naive Bayes dengan Seleksi Fitur Information Gain Fahmi Achmad Fauzi; Muhammad Tanzil Furqon; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tobacco plants are plantation products but not food crops, their leaves are usually used as the main ingredient in the making of cigarettes and cigars. Tobacco cultivation has been known for a long time in Indonesia, the cultivation of tobacco from generation to generation has resulted in the emergence of many new varieties in various regions in Indonesia. The number of tobacco varieties can be grouped by cultivation and type. The large number of tobacco varieties makes it difficult for farmers to distinguish the types of tobacco plants because the morphology and biology between tobacco plants are almost similar, so to make it easier to determine the type of tobacco plants, a system with a classification method is needed. One of the classification methods is the Naive Bayes algorithm. In this study, 11 classes were used and 19 features were used. In addition to classification, the feature selection method is also used to get a good combination of features and accuracy values, Information Gain used as the feature selection method. In the evaluation, the K-fold cross validation method is used to eliminate doubts on the data with k = 10. The result of all the tests carried out, the highest average accuracy for all balanced class tests was 52.72% using 17 features. Meanwhile, the highest accuracy of all unbalanced class tests is 64.06% when using 15 features.
Prediksi Omzet Restoran Haltoy Corner menggunakan Metode Recurrent Extreme Learning Machine (RELM) Ridho Ghiffary Muhammad; Muhammad Tanzil Furqon; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Haltoy Corner Restaurant is a new restaurant in wonosobo city that is famous for its beautiful scenery. Currently, Haltoy Corner is still not able to do the management of the number of employees and the allocation of turnover well. This led to the need for a turnover prediction system for Haltoy Corner to help optimize the number of employees to be employed. Extreme Learning Machine (ELM) is one of the prediction methods that have good accuracy and relatively fast training time, but in ELM the sequence of data has no effect so it can affect the accuracy for dataset timeseries such as Haltoy Corner turnover data. ELM developed a method to overcome this with Recurrent Extreme Learning Machine (RELM), this method adds recurrent to ELM so that it is better for dataset timeseries. The flow to conduct this research starts from data normalization, data training, data testing, data denormalization and finally the calculation of evaluation value. Based on the results of tests conducted using Haltoy Corner turnover data, an error value with Mean Absolute Precentage Error (MAPE) was obtained at the most optimal of 31.677%, with the number of eight features, the number of hidden neurons three, the number of context neurons five, and the comparison of the number of training data with data testing of 90%:10%.
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