Deteksi Anomali Pada Pemakaian Air Pelanggan PDAM Surya Sembada Kota Surabaya Menggunakan Kohonen SOM dan Local Outlier Factor

Kehilangan air dalam distribusi merupakan masalah yang cukup serius, kerugian yang disebabkan oleh tingkat kehilangan PDAM Surya Sembada Kota Surabaya mencapai 2 Miliar Rupiah.Pada tahun 2011 terjadi pencurian air sebanyak 100 kasus. Deteksi anomali pada penelitian dilakukan menggunakan data selama Maret 2017 – Februari 2018. Data yang digunakan adalah pemakaian air kemudian didapatkan variabel-variabel rata-rata pemakaian air, maksimal pemakaian air, dan deviasi standar pemakaian air. Algoritma Kohonen-SOM mendapatkan 45 kelompok yang dianggap kelompok anomali dengan kriteria silhouette width kurang dari rata-rata silhouette width pada kelompok yang ter-bentuk. Terdapat 45 kelompok yang terduga anomali. Local Outlier Factor menghasilkan 1229 kejadian konsumsi yang tidak normal, 1229 kejadian tersebut terdiri dari 579 rumah tangga atau pelanggan. Perhitungan frekuensi yang dilakukan mendapatkan 42 pelanggan yang terduga anomali. Hasil deteksi anomali dengan metode PDAM dari 42 pelanggan te-rsebut hanya 16 yang terde-teksi. Hal tersebut dikarenakan metode PDAM gagal menangkap perilaku konsumsi yang aneh seperti konsumsi yang konstan setiap bulan. Karakteristik pelanggan yang terdeteksi anomali adalah mempunyai rata-rata pemakaian lebih dari rata-rata pemakaian golongan dan sub-zona. ============================================================ The loss of water in the distribution is a serious problem in PDAM Surabaya, the loss caused by loss rate of PDAM Surabaya reach 2 billion Rupiah. In 2011 there was a theft of 100 cases. Detection of anomalies in the study was conducted using data during March 2017 - February 2018. The data used is water consumption then obtained the variables likemean of water use, maximum water use, and standart deviation of water usage. The Kohonen-SOM algorithm obtained 45 groups considered anomalous group with silhouette width criteria less than the silhouette width average in the group formed. There are 45 groups of unexpected anomalies. Local Outlier Factor produced 1229 unusual consumption events, 1229 incidents consisting of 579 households or customers. The frequency calculation performed gets 42 suspected anomaly customers. The result of anomaly detection with PDAM method from 42 customers was only 16 detected. This is because the PDAM method fails to capture strange consumption behaviors such as cons-tant consumption every month. The characteristic of the customer detected by the anomaly is to have an average of more than average usage of classes and sub-zones

FORECASTING THE OCCUPANCY RATE OF STAR HOTELS IN BALI USING THE XGBOOST AND SVR METHODS

The hotel occupancy rate indicator has become a concern in recent years as it goes hand in hand with the rapid growth of the global tourism industry. A way to maintain or even improve this indicator is to carry out managerial planning using forecasting methods. The forecasting methods used in this research are XGBoost and SVR. The advantage of this modelling is that it achieves high accuracy and processing speed. Meanwhile, the benefit of SVR is that it will produce good prediction because can overcome overfitting. The steps in this research are exploring data, separating training data and testing data, transforming data, modelling data, forecasting data, and evaluating forecasting results using RMSE, MAE, and MAPE. The results show that MAPE value from both methods is smaller than 10%, which means that both methods can predict the occupancy rate of star hotels in Bali very accurately. Apart from that, the SVR method has smaller values for all model evaluation criteria than the XGBoost method, which means that the SVR method is better than XGBoost for predicting the occupancy rate of star hotels in Bali

Multi-label Classification Using Vector Generalized Additive Model via Cross-Validation

Multi-label classification is a unique challenge in machine learning designed for two targets with each containing one or multiple classes. This problem can be resolved using several methods, including the classification of the targets individually or simultaneously. However, most models cannot classify the target simultaneously, and this is not expected to happen in the modeling rule. This study was conducted to propose a novel solution in the form of a Vector Generalized Additive Model Using Cross-Validation (VGAMCV) to address these problems. The proposed method leverages the Vector Generalized Additive Model (VGAM), which is a semi-parametric model combining both parametric and non-parametric components as the underlying base model. Cross-validation was also applied to tune the parameters to optimize the performance of the method. Moreover, the methodology of VGAMCV was compared with a tree-based model, Random Forest, commonly used in multi-label classification to evaluate its effectiveness based on fourteen metric scores. The results showed positive outcomes as indicated by 0.703 average accuracy and 0.601 Area Under Curve (AUC) recorded, but these improvements were not statistically significant. Meanwhile, the method offered a viable alternative for multi-label classification tasks, and its introduction served as a contribution to the expanding repertoire of methods available for this purpose

Batas Atas Ukuran Risiko Agregat Pada Portofolio Saham INDF.JK dan ICBP.JK

Pada investasi agregat aset finansial, setiap aset tunggal dapat memunculkan potensi risiko kerugian yang harus ditanggung oleh investor. Pada kondisi ini, untuk memprediksi nilai risiko kerugian dapat digunakan konsep risiko agregat. Prediksi nilai risiko dapat diukur melalui suatu ukuran risiko, salah satunya adalah Value at Risk (VaR). Namun, VaR tidak selalu memenuhi sifat subaditif, sehingga VaR bukan merupakan ukuran risiko yang koheren. Ukuran risiko lain sebagai alternatif pengganti VaR adalah Expected Shortfall (ES). Kelebihan utama ES dibandingkan VaR adalah ES telah memenuhi sifat subaditif, sehingga ES adalah ukuran risiko yang koheren. Untuk memprediksi nilai risiko agregat menggunakan VaR maupun ES, dibutuhkan fungsi distribusi bersama dari risiko agregat tersebut. Akan tetap cukup sulit untuk menentukan fungsi distribusi bersama risiko agregat yang disusun oleh beberapa risiko tunggal yang tidak saling bebas. Alternatif yang dapat digunakan apabila fungsi distribusi bersama risiko agregat sulit diperoleh adalah dengan menghitung batas atas risiko agregat dengan memanfaatkan sifat komonotonik dan convex order. Penelitian ini bertujuan untuk mengukur nilai batas risiko agregat menggunakan ukuran risiko ES untuk investasi agregat pada saham PT. Indofood Sukses Makmur Tbk (INDF.JK) dan PT Indofood CBP Sukses Makmur Tbk (ICBP.JK). Berdasarkan hasil analisis menggunakan data return saham INDF.JK dan ICBP.JK periode 02/01/21 – 17/09/21, nilai batas atas ukuran risiko aregat VaR dan ES pada portofolio saham untuk tingkat kepercayaan 95% dan holding period 1 hari masing-masing adalah -0,05231 dan -0,07731

Identifikasi Penyakit Daun Jeruk Siam Menggunakan Convolutional Neural Network (CNN) dengan Arsitektur EfficientNet

Jeruk siam menjadi salah satu komoditas hortikultura yang memegang peranan utama dalam sektor pertanian Indonesia dengan jumlah produksi yang mencapai 2 juta ton setiap tahunnya. Namun, produksi jeruk siam rentan terhadap serangan hama dan penyakit, terutama pada bagian daun. Penyakit yang umum terjadi termasuk Blackspot Leaf, Canker Leaf, Greening Leaf, Powdery Mildew, dan Citrus Leafminer. Pada umunya identifikasi penyakit pada tanaman jeruk dilakukan secara manual sehingga penentuan penyakit cenderung subyektif. Oleh karena itu, diperlukan solusi otomatis dalam mendeteksi penyakit pada daun jeruk. Tujuan penelitian yaitu untuk mengidentifikasi penyakit yang menyerang daun jeruk menggunakan metode deep learning yaitu CNN dengan arsitektur EfficientNetB3. Dataset yang digunakan adalah citra penyakit daun jeruk yang diambil langsung dari kebun jeruk yang dibagi menjadi 6 kelas seperti pada penyakit yang disebutkan di atas. Hasil penelitian menggunakan skenario epoch 10 dengan optimizer Adam memperoleh hasil akurasi terbaik yaitu 0,98 (98%)

ANALYSIS OF THE RELATIONSHIP BETWEEN CHARACTERISTICS OF TEENAGERS AND FAMILY FUNCTIONS ON TEENAGERS’ BEHAVIOR FOR CONSUMING DRUGS IN EAST JAVA

This research aims to analyze the relationship between the characteristics of teenagers and family functions as well as the understanding of narcotics, psychotropics, and addictive substances on the behavior of teenagers consuming drugs in East Java as a response variable with a binary scale. The data source obtained through secondary data from the Performance Survey and Accountability Program 2019 with the observation unit teenagers aged 10-24 years. The sample used is 4,649. The analytical method used is descriptive statistics, the Chi-square method, and Odds Ratio (OR). The percentage of adolescent consuming drugs is 4.1% descriptively. The relationship analysis shows that the variables significantly related to young people's behavior in consuming drugs are gender, place of residence, level of education, age group, religious values, and psychological consequences. From the OR figures concluded that young males are 3.2 times more at risk of consuming drugs than young females. From the aspect of family function, it can be inferred that the percentage of young substance abusers from families who apply religious values is greater than those who do not. The findings of this research show that the risk of young people from families who practice religious functions becoming substance abusers are 1.61 times more compared to families who do not practice it. The understanding of drugs is not always related to teenagers' behavior in consuming drugs, because those who understand the psychological consequences of substance abuse are also 1.64 times more at risk of using drugs compared to teenagers who do not understand

Forecasting With Recurrent Neural Networks For Intermittent Demand Data

Data permintaan berselang biasanya disebut data permintaan pelanggan atau data penjualan. Dataset mencatat nilai bukan nol jika ada permintaan. Jika tidak ada permintaan, catatan set data adalah nilai nol. Masalah umumnya adalah bahwa permintaan tidak selalu terus-menerus tetapi terputus-putus. Karakteristik ini membuat data yang terputus-putus sulit digunakan untuk prediksi. Metode standar yang digunakan untuk memprediksi data permintaan berselang antara lain Croston dan single exponential smoothing (SES). Croston dan SES biasanya menghasilkan prakiraan statis. Penelitian ini memanfaatkan metode deep learning Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), dan Long Short Term Memory (LSTM) untuk memprediksi data intermiten. Studi simulasi dilakukan dengan menghasilkan dataset dengan 6 parameter desain yang berbeda dan dengan 50 pengulangan. Selain itu studi empiris menggunakan data dari website Kaggle. Penelitian ini mengukur kinerja prediksi data permintaan intermiten dengan RNN, GRU, dan LSTM, dibandingkan dengan Croston dan SES sebagai metode benchmark. Pengukuran kinerja evaluasi menggunakan mean absolute error (MAE) dan root mean squared scaled error (RMSSE). Dalam studi simulasi, sebagian besar metode jaringan saraf berulang dapat bekerja dengan baik dalam skor MAE. Untuk studi empiris, metode jaringan saraf berulang mengungguli metode konvensional dalam skor MAE untuk semua kumpulan data. Namun, metode konvensi Croston bekerja dalam skor RMSSE untuk sebagian besar studi simulasi dan satu studi empiris. ========================================================================================================= Intermittent demand data is usually called customer demand data or sales data. The dataset will record a nonzero value if there is a demand. If there is no demand, the dataset records are zero values. The general problem is that demand is not always continuous but intermittent. This characteristic makes intermittent data difficult to use for prediction. Standard methods used to predict intermittent demand data include Croston, single exponential smoothing (SES), and others. The Croston and SES typically produce static forecasts. This study utilized deep learning methods recurrent neural network (RNN), gated recurrent units (GRU), and long short-term memory (LSTM) to predict intermittent data. The simulation study was carried out by generating datasets with 6 different design parameters and with 50 repetitions. Besides, the empirical study used data from the Kaggle website. This study measured the performance of predicting intermittent demand data by RNN, GRU, and LSTM, comparison to Croston and SES as the benchmark methods. The performance measurements included the evaluation of mean absolute error (MAE) and root mean squared scaled error (RMSSE). In simulation studies, most recurrent neural network methods can perform well in MAE scores. For the empirical study, recurrent neural network methods outperform conventional methods in MAE scores for all datasets. Yet, the convention method of Croston works in RMSSE scores for most simulation studies and one empirical study