Judul: Application of Generalized LASSO in Outlier Detection in House Price Modelling in West Java
Abstrak: Pencilan adalah amatan yang secara signifikan berbeda jauh dari amatan lainnya dalam sebuah data. Keberadaan pencilan ini dapat mengganggu pendugaan koefisien dalam regresi. Pendeteksian pencilan bisa dilakukan dengan menggunakan metode Generalized Least Absolute Shrinkage Selection Operator (Generalized LASSO). Generalized LASSO adalah sebuah metode yang mengaplikasikan nilai D sebagai matriks penalti (D) untuk regresi lasso. Variasi dari nilai D beragam disesuaikan dengan kebutuhan penggunaan, salah satunya untuk mendeteksi pencilan. Pengaplikasian metode Generalized LASSO untuk mendeteksi pencilan pada pemodelan harga rumah di Jawa Barat tahun 2023 menunjukkan bahwa metode ini cukup baik dalam mendeteksi pencilan sehingga bisa digunakan sebagai alternatif untuk pendeteksian pencilan. Pemilihan lamda optimal berpengaruh terhadap jumlah amatan yang terdeteksi pencilan. Semakin besar nilai lamda yang digunakan maka model akan menjadi lebih sederhan dan amatan yang terdeteksi pencilan semakin sedikit. Sebaliknya, semakin kecil nilai lamda yang digunakan maka model akan menjadi semakin kompleks dan amatan yang terdeteksi pencilan lebih banyak., Outliers are observations that are significantly different from other observations in the data. The existence of this outlier can interfere with the estimation of coefficients in regression. Outlier detection can be done using the Generalized Least Absolute Shrinkage Selection Operator (Generalized LASSO) method. Generalized LASSO is a method that applies the D value as a penalty matrix (D) for lasso regression. Variations in the D value vary according to usage needs, one of which is to detect outliers. The application of the Generalized LASSO method to detect outliers in house price modeling in West Java in 2023 shows that this method is quite good at detecting outliers so that it can be used as an alternative for outlier detection. Choosing the optimal lambda affects the number of observations that detect outliers. The greater the lambda value used, the simpler the model will be and the fewer outliers detected will be observed. On the other hand, the smaller the lambda value used, the more complex the model will become and more outliers will be detected.
Keyword: deteksi pencilan, generalized LASSO, pemodelan harga rumah, generalized LASSO, house price modelling, outlier detection

Judul: Generalized LASSO in Identifiying Categoric Variables with the same Affection for House Prices in West Java in 2023
Abstrak: Perumahan merupakan sebuah lingkungan tempat tinggal yang terdiri dari sekelompok atau sekumpulan rumah. Rumah sangat berperan penting sebagai tempat berlindung bagi suatu individu dan sarana terbentuknya generasi yang unggul. Dalam menentukan tempat tinggal, manusia cenderung meperhatikan kualitas, kenyamanan, harga, maupun lokasi. Umumnya, besaran harga rumah dipengaruhi oleh banyak faktor, yakni spesifikasi dan lokasi dari rumah tersebut. Faktor ini dapat berupa peubah kategorik maupun numerik. Penelitian ini bertujuan mengidentifikasi kesamaan pengaruh antar kategori pada peubah kategorik yang diduga memengaruhi harga rumah. Peubah kategorik yang digunakan ialah daya listrik, jumlah kamar tidur, jumlah kamar mandi, jumlah tempat kendaraan, dan sertifikasi. Selain itu, terdapat dua peubah numerik yang digunakan yaitu luas bangunan dan luas tanah. Data yang dianalisis sebanyak 300 data harga rumah di Jawa Barat tahun 2023. Provinsi Jawa Barat memiliki jumlah penduduk terbesar pertama mencapai 18% dari total penduduk di Indonesia. Jumlah rumah tangga yang memiliki rumah dengan status kepemilikan milik sendiri mencakup 83% dari total rumah tangga yang ada per tahun 2022. Metode yang digunakan adalah generalized LASSO yang dapat memperhitungkan faktor kedekatan antar kategori. Kategori jumlah kamar tidur enam dan tujuh, jumlah kamar tidur sembilan dan sepuluh, kategori jumlah kamar mandi enam dan delapan, kategori jumlah tempat kendaraan dua dan tiga, jumlah tempat kendaraan enam dan tujuh, serta kategori sertifikasi SHGB dan PPJB memiliki pengaruh yang sama terhadap harga rumah di Jawa Barat tahun 2023., Housing is a residential environment consisting of a group of houses. The house plays an important role as a place to live for an individual and a means of forming a superior generation. In determining a place to live, humans tend to pay attention to quality, comfortable, price, or location. Generally, house prices are affected by many factors, such as the specification and location. This study uses to identify the similarity of influence between categories on categoric variables that are thought to affect house prices. The categorical variables used are electrical power, total number of bedroom, total number of bathroom, total number of vehicle spaces, and certificate. In addition, numerical variables used are building and surface area. The data analyzed were 300 data on house prices in West Java in 2023. West Java Province has the largest total population reaching 18% of the total population in Indonesia. The number of households who owning their own houses reaching 83% of the total households in 2022. The method used is generalized LASSO which can take into account the proximity factor between categories. The number of bedroom of six-seven, the number of bedroom of nine-ten, the number of bathroom of six-eight, the number of vehicle spaces of two-three, the number of vehicle spaces of six-seven, and certificate of SHGB-PPJB have the same effect on house prices in 2023.
Keyword: generalized LASSO, house prices, households, housing, west java

Judul: Bioelectricity Production from Citrobacter freundii GBH253 and Chlorella pyrenoidosa on Microbial Fuel Cell Anode with Different Carbon Sources
Abstrak: Microbial Fuel Cell (MFC) development has significantly increased from various aspects to get higher and more stable electricity. Developing MFC on microorganism aspect is very important because it plays a crucial role in the MFC system. C. freundii GBH253 is one of the potential exoelectrogen bacterium that could be developed for the MFC system. Integrating C. freundii GBH253 and electricity-producing microalgae, such as C. pyrenoidosa, in the anode of MFC could get more stable and higher electricity. In this study, C. freundii GBH253 and C. pyrenoidosa were coupled to produce electricity from different substrates. C. freundii GBH253 and C. pyrenoidosa were tested on three different media types before being tested on MFC. They were also examined for the co-cultivation test to determine the symbiotic relationship between the two microorganisms. The MFC test was conducted using the Randomized Block Design Factorial and the Tukey test to see significant differences from each treatment. The parameters observed in this study were voltage, power density, Chemical Oxygen Demand (COD), Coulombic Efficiency (CE), pH, and volatile fatty acid (VFA). C. freundii GBH253 cannot grow on microalgae medium, while C. pyrenoidosa could grow on bacterial medium and a mixture of bacterium and microalgae media. The bacterium gained benefit from co-culture with microalgae. In the MFC test, electricity was generated in all treatments, which bacterium + microalgae with acetate treatment could generate power density up to 211,97 mW/m2 and the most stable compared to others. Production of electricity in bacterium-microalgae combination dominated by the bacterium; hence, microalgae still help bacterium by producing electricity and raised the COD value. The pH value of all treatments was higher than the initial pH. Acetate and phenol were VFA produced in all treatments, whereas butyric acid and propionic acid were found in some treatments. Pearson correlation showed that some VFA highly correlated with power density.
Keyword: Chlorella pyrenoidosa, Substrate, Microbial Fuel Cell, Electricity Producing Microalgae, Volatile Fatty Acid, Citrobacter freundii