Judul: Analysis of Price Volatility and Vertical Integration of Cooking Oil Market in Indonesia
Abstrak: Fenomena kenaikan dan penurunan harga minyak goreng yang berbeda setiap jenisnya turut berperan sebagai penyumbang angka inflasi dalam kelompok pangan bergejolak di Indonesia. Perubahan harga yang tidak stabil tersebut menjadi tantangan tersendiri bagi produsen, pedagang besar, dan pedagang eceran. Penelitian ini bertujuan menganalisis volatilitas dan hubungan integrasi vertikal pada harga minyak goreng kemasan premium, kemasan sederhana, dan kemasan curah pada setiap tingkat harga pasar di produsen, pedagang besar, dan pedagang eceran. Penelitian ini menggunakan harga minyak goreng mingguan nasional tahun 2019-2023 diperoleh dari Pusat Informasi Harga Pangan Strategis. Analisis yang dilakukan yakni Koefisien Variasi untuk melihat fluktuasi harga, model ARCH/GARCH untuk menganalisis volatilitas, dan model VECM untuk menganalisis integrasi vertikal. Hasil penelitian menunjukkan harga minyak goreng di setiap jenis kualitas memiliki perbedaan nilai fluktuasi. Volatilitas sangat tinggi terjadi pada harga minyak goreng premium dan curah di tingkat pedagang eceran, sedangkan volatilitas tinggi terjadi pada harga minyak goreng curah di tingkat pedagang besar. Hasil VECM menunjukkan terdapat hubungan jangka panjang dan jangka pendek antar variabel harga minyak goreng pada tiga tingkat pasar berbeda., The phenomenon of increasing and decreasing cooking oil prices that are different for each type contributes to the inflation rate in the volatile food group in Indonesia. The unstable price changes are a challenge for producers, wholesalers, and retailers. This study aims to analyze the volatility and vertical integration relationship in the price of premium packaged cooking oil, simple packaging, and bulk packaging at each market price level at producers, wholesalers, and retailers. This study uses weekly national cooking oil prices for 2019-2023 obtained from the Strategic Food Price Information Center. The analysis carried out is the Coefficient of Variation to see price fluctuations, ARCH/GARCH models to analyze volatility, and VECM models to analyze vertical integration. The results show that cooking oil prices in each type of quality have different fluctuation values. Very high volatility occurs in the price of premium and bulk cooking oil at the retail level, while high volatility occurs in the price of bulk cooking oil at the wholesale level. VECM results show that there are long-run and short-run relationships between cooking oil price variables at three different market levels.
Keyword: Volatilitas, harga, integrasi, minyak goreng

Judul: Analysis of National Strategic Food Price Volatility and Its Effect on Inflation
Abstrak: Pangan merupakan kebutuhan primer yang harus terjaga stabilitas harganya. Beberapa periode terakhir harga sejumlah komoditas pangan strategis mengalami peningkatan. Penelitian ini bertujuan untuk menganalisis tingkat volatilitas harga minyak goreng, beras, cabai merah, bawang merah dan bawang putih serta pengaruhnya terhadap inflasi. Data yang digunakan adalah data harga kelima komoditas yang diperoleh dari Pusat Informasi Harga Pangan Strategis dan data inflasi diperoleh dari Bank Indonesia. Analisis yang dilakukan yakni Coefficient of Variation untuk melihat disparitas harga di seluruh provinsi di Indonesia dan pemodelan ARCH-GARCH untuk menganalisis volatilitas. Error Correction Model digunakan untuk menganalisis pengaruh volatilitas harga pangan terhadap inflasi. Hasil penelitian menunjukkan terdapat disparitas harga pangan antar waktu dan antar wilayah di Indonesia. Volatilitas harga kelima komoditas terjadi pada data harga tingkat nasional dan pada sejumlah wilayah amatan. Volatilitas harga minyak goreng serta beras berpengaruh signifkan terhadap inflasi pada jangka panjang. Sementara pada jangka pendek, volatilitas harga cabai merah dan volatilitas bawang merah bulan sebelumnya signifikan mempengaruhi tingkat inflasi, Food is a primary necessity that requires accessibility and price stability. In recent periods, the prices of some strategic food commodities have increased. The aim of this research is to analyze the level of price volatility of cooking oil, rice, red chili, shallots, and garlic, as well as their effect on inflation. The data used include the prices of these five commodities obtained from the Strategic Food Price Information Center and the inflation rate data obtained from the Bank of Indonesia. The analysis conducted includes the Coefficient of Variation to observe price disparities across all provinces in Indonesia and the ARCH-GARCH modeling to analyze volatility. The Error Correction Model is used to analyze the effect of food price volatility on inflation. The results show that price disparities occurs over time and across regions in Indonesia. Volatility in the prices of these five commodities occurs both at the national price level and in some observed regions. The price volatility of cooking oil and rice has a significant long-term effect on inflation. Meanwhile, in the short term, price volatility of red chili and shallots from the previous month have a significant effect on the inflation rate.
Keyword: ARCH-GARH, ECM, inflasi, pangan, volatilitas

Judul: Construction of Strongly Optimal Binary Linear Code with Minimum Distance 9 and 11
Abstrak: A binary linear code of length n over q F is a subspace of n q F . A code has three parameters that attached to it, namely length, dimension, and minimum distance. A code with length n, dimension k and minimum distance d is often called [n, k, d ]-code. Usually, when two parameters are given, then we want to find a code that has the best value for the last parameter. Based on Gilbert- Varshamov bound, if a [n, k, d ]-code exists and can not be expanded, we call it a strongly optimal code. In this paper, we construct strongly optimal code with minimum distance 9 and 11. In constructing the code, we created a theorem and algorithm based on Gilbert-Varshamov bound before we implement the algorithm to MAPLE programming language. Because of computational limitations, the program can only construct up to k = 10 for d = 9 and k = 12 for d = 11.
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